Overview

Brought to you by YData

Dataset statistics

Number of variables264
Number of observations604626
Missing cells118658887
Missing cells (%)74.3%
Total size in memory1.2 GiB
Average record size in memory2.1 KiB

Variable types

Numeric40
Unsupported92
Text123
Boolean9

Dataset

DescriptionEntomology NMNH Extant Extant Specimen Records 0052484-241126133413365
URLhttps://doi.org/10.15468/dl.ptewed

Alerts

license has constant value "CC0_1_0" Constant
publisher has constant value "National Museum of Natural History, Smithsonian Institution" Constant
institutionID has constant value "urn:lsid:biocol.org:col:34871" Constant
collectionID has constant value "urn:uuid:18e3cd08-a962-4f0a-b72c-9a0b3600c5ad" Constant
institutionCode has constant value "USNM" Constant
collectionCode has constant value "ENT" Constant
datasetName has constant value "NMNH Extant Biology" Constant
occurrenceStatus has constant value "PRESENT" Constant
verbatimLabel has constant value "-11.7815" Constant
materialSampleID has constant value "-76.7017" Constant
verbatimDepth has constant value "220m inside cave entrance" Constant
verbatimCoordinateSystem has constant value "Degrees Minutes Seconds" Constant
verbatimSRS has constant value "1973-05-08" Constant
footprintSRS has constant value "128.0" Constant
footprintSpatialFit has constant value "128.0" Constant
georeferencedDate has constant value "5.0" Constant
earliestEraOrLowestErathem has constant value "Animalia" Constant
latestEraOrHighestErathem has constant value "Arthropoda" Constant
earliestPeriodOrLowestSystem has constant value "Insecta" Constant
group has constant value "Florida" Constant
formation has constant value "Pinellas" Constant
verbatimIdentification has constant value "SPECIES" Constant
identifiedByID has constant value "ACCEPTED" Constant
taxonConceptID has constant value "StillImage" Constant
acceptedNameUsage has constant value "False" Constant
nameAccordingTo has constant value "1.0" Constant
namePublishedIn has constant value "54.0" Constant
namePublishedInYear has constant value "216.0" Constant
subtribe has constant value "EML" Constant
subgenus has constant value "True" Constant
verbatimTaxonRank has constant value "PER" Constant
nomenclaturalCode has constant value "PER.16_1" Constant
nomenclaturalStatus has constant value "PER.16.6_1" Constant
taxonRemarks has constant value "Huarochiri" Constant
subgenusKey has constant value "Insecta" Constant
protocol has constant value "EML" Constant
projectId has constant value "roseni" Constant
isSequenced has constant value "False" Constant
Unnamed: 229 has constant value "StillImage;StillImage;StillImage" Constant
Unnamed: 230 has constant value "True" Constant
Unnamed: 231 has constant value "False" Constant
Unnamed: 234 has constant value "1.0" Constant
Unnamed: 235 has constant value "54.0" Constant
Unnamed: 236 has constant value "216.0" Constant
Unnamed: 241 has constant value "1364691.0" Constant
Unnamed: 242 has constant value "Aphytis roseni" Constant
Unnamed: 246 has constant value "EML" Constant
Unnamed: 248 has constant value "2024-12-02T11:48:23.416Z" Constant
Unnamed: 252 has constant value "False" Constant
Unnamed: 254 has constant value "NORTH_AMERICA" Constant
Unnamed: 261 has constant value "PER.17.1.5_1" Constant
Unnamed: 262 has constant value "Lagunas" Constant
Unnamed: 263 has constant value "NE" Constant
hasGeospatialIssues is highly imbalanced (98.7%) Imbalance
accessRights has 604626 (100.0%) missing values Missing
bibliographicCitation has 604626 (100.0%) missing values Missing
language has 604626 (100.0%) missing values Missing
references has 604626 (100.0%) missing values Missing
rightsHolder has 604626 (100.0%) missing values Missing
type has 604626 (100.0%) missing values Missing
datasetID has 604626 (100.0%) missing values Missing
ownerInstitutionCode has 604626 (100.0%) missing values Missing
informationWithheld has 604626 (100.0%) missing values Missing
dataGeneralizations has 604626 (100.0%) missing values Missing
dynamicProperties has 604626 (100.0%) missing values Missing
catalogNumber has 233418 (38.6%) missing values Missing
recordNumber has 604589 (> 99.9%) missing values Missing
recordedBy has 203336 (33.6%) missing values Missing
recordedByID has 604626 (100.0%) missing values Missing
organismQuantity has 604626 (100.0%) missing values Missing
organismQuantityType has 604626 (100.0%) missing values Missing
sex has 384462 (63.6%) missing values Missing
lifeStage has 184129 (30.5%) missing values Missing
reproductiveCondition has 604626 (100.0%) missing values Missing
caste has 604626 (100.0%) missing values Missing
behavior has 604626 (100.0%) missing values Missing
vitality has 604626 (100.0%) missing values Missing
establishmentMeans has 604626 (100.0%) missing values Missing
degreeOfEstablishment has 604626 (100.0%) missing values Missing
pathway has 604626 (100.0%) missing values Missing
georeferenceVerificationStatus has 604626 (100.0%) missing values Missing
preparations has 42051 (7.0%) missing values Missing
disposition has 604626 (100.0%) missing values Missing
associatedOccurrences has 604626 (100.0%) missing values Missing
associatedReferences has 604626 (100.0%) missing values Missing
associatedSequences has 604626 (100.0%) missing values Missing
associatedTaxa has 604626 (100.0%) missing values Missing
otherCatalogNumbers has 604626 (100.0%) missing values Missing
occurrenceRemarks has 459276 (76.0%) missing values Missing
organismID has 604626 (100.0%) missing values Missing
organismName has 604626 (100.0%) missing values Missing
organismScope has 604626 (100.0%) missing values Missing
associatedOrganisms has 604626 (100.0%) missing values Missing
previousIdentifications has 604626 (100.0%) missing values Missing
organismRemarks has 604626 (100.0%) missing values Missing
materialEntityID has 604626 (100.0%) missing values Missing
materialEntityRemarks has 604626 (100.0%) missing values Missing
verbatimLabel has 604625 (> 99.9%) missing values Missing
materialSampleID has 604625 (> 99.9%) missing values Missing
eventID has 604626 (100.0%) missing values Missing
parentEventID has 604626 (100.0%) missing values Missing
eventType has 604626 (100.0%) missing values Missing
fieldNumber has 600377 (99.3%) missing values Missing
eventDate has 239769 (39.7%) missing values Missing
eventTime has 604626 (100.0%) missing values Missing
startDayOfYear has 270965 (44.8%) missing values Missing
endDayOfYear has 270965 (44.8%) missing values Missing
year has 240229 (39.7%) missing values Missing
month has 254573 (42.1%) missing values Missing
day has 314935 (52.1%) missing values Missing
verbatimEventDate has 396306 (65.5%) missing values Missing
habitat has 604427 (> 99.9%) missing values Missing
samplingProtocol has 604626 (100.0%) missing values Missing
sampleSizeValue has 604626 (100.0%) missing values Missing
sampleSizeUnit has 604626 (100.0%) missing values Missing
samplingEffort has 604626 (100.0%) missing values Missing
fieldNotes has 604626 (100.0%) missing values Missing
eventRemarks has 604626 (100.0%) missing values Missing
locationID has 603581 (99.8%) missing values Missing
higherGeographyID has 604626 (100.0%) missing values Missing
higherGeography has 156072 (25.8%) missing values Missing
continent has 199137 (32.9%) missing values Missing
waterBody has 604626 (100.0%) missing values Missing
islandGroup has 602107 (99.6%) missing values Missing
island has 595261 (98.5%) missing values Missing
countryCode has 163440 (27.0%) missing values Missing
stateProvince has 173217 (28.6%) missing values Missing
county has 254826 (42.1%) missing values Missing
municipality has 604626 (100.0%) missing values Missing
locality has 158340 (26.2%) missing values Missing
verbatimLocality has 604626 (100.0%) missing values Missing
verbatimElevation has 594692 (98.4%) missing values Missing
verticalDatum has 604626 (100.0%) missing values Missing
verbatimDepth has 604620 (> 99.9%) missing values Missing
minimumDistanceAboveSurfaceInMeters has 604624 (> 99.9%) missing values Missing
maximumDistanceAboveSurfaceInMeters has 604626 (100.0%) missing values Missing
locationAccordingTo has 604626 (100.0%) missing values Missing
locationRemarks has 604626 (100.0%) missing values Missing
decimalLatitude has 285575 (47.2%) missing values Missing
decimalLongitude has 285575 (47.2%) missing values Missing
coordinateUncertaintyInMeters has 592674 (98.0%) missing values Missing
coordinatePrecision has 604626 (100.0%) missing values Missing
pointRadiusSpatialFit has 604624 (> 99.9%) missing values Missing
verbatimCoordinateSystem has 604625 (> 99.9%) missing values Missing
verbatimSRS has 604625 (> 99.9%) missing values Missing
footprintWKT has 604626 (100.0%) missing values Missing
footprintSRS has 604625 (> 99.9%) missing values Missing
footprintSpatialFit has 604625 (> 99.9%) missing values Missing
georeferencedBy has 604623 (> 99.9%) missing values Missing
georeferencedDate has 604625 (> 99.9%) missing values Missing
georeferenceProtocol has 366755 (60.7%) missing values Missing
georeferenceSources has 604624 (> 99.9%) missing values Missing
georeferenceRemarks has 596178 (98.6%) missing values Missing
geologicalContextID has 604626 (100.0%) missing values Missing
earliestEonOrLowestEonothem has 604626 (100.0%) missing values Missing
latestEonOrHighestEonothem has 604624 (> 99.9%) missing values Missing
earliestEraOrLowestErathem has 604624 (> 99.9%) missing values Missing
latestEraOrHighestErathem has 604624 (> 99.9%) missing values Missing
earliestPeriodOrLowestSystem has 604624 (> 99.9%) missing values Missing
latestPeriodOrHighestSystem has 604624 (> 99.9%) missing values Missing
earliestEpochOrLowestSeries has 604626 (100.0%) missing values Missing
latestEpochOrHighestSeries has 604622 (> 99.9%) missing values Missing
earliestAgeOrLowestStage has 604624 (> 99.9%) missing values Missing
latestAgeOrHighestStage has 604626 (100.0%) missing values Missing
lowestBiostratigraphicZone has 604626 (100.0%) missing values Missing
highestBiostratigraphicZone has 604624 (> 99.9%) missing values Missing
lithostratigraphicTerms has 604622 (> 99.9%) missing values Missing
group has 604625 (> 99.9%) missing values Missing
formation has 604625 (> 99.9%) missing values Missing
member has 604624 (> 99.9%) missing values Missing
bed has 604624 (> 99.9%) missing values Missing
identificationID has 604626 (100.0%) missing values Missing
verbatimIdentification has 604624 (> 99.9%) missing values Missing
identificationQualifier has 603189 (99.8%) missing values Missing
typeStatus has 486591 (80.5%) missing values Missing
identifiedBy has 454955 (75.2%) missing values Missing
identifiedByID has 604624 (> 99.9%) missing values Missing
dateIdentified has 604626 (100.0%) missing values Missing
identificationReferences has 604626 (100.0%) missing values Missing
identificationVerificationStatus has 604622 (> 99.9%) missing values Missing
identificationRemarks has 604622 (> 99.9%) missing values Missing
taxonID has 604624 (> 99.9%) missing values Missing
scientificNameID has 604626 (100.0%) missing values Missing
parentNameUsageID has 604626 (100.0%) missing values Missing
originalNameUsageID has 604626 (100.0%) missing values Missing
nameAccordingToID has 604626 (100.0%) missing values Missing
namePublishedInID has 604624 (> 99.9%) missing values Missing
taxonConceptID has 604625 (> 99.9%) missing values Missing
acceptedNameUsage has 604624 (> 99.9%) missing values Missing
parentNameUsage has 604623 (> 99.9%) missing values Missing
originalNameUsage has 604624 (> 99.9%) missing values Missing
nameAccordingTo has 604624 (> 99.9%) missing values Missing
namePublishedIn has 604624 (> 99.9%) missing values Missing
namePublishedInYear has 604624 (> 99.9%) missing values Missing
superfamily has 604624 (> 99.9%) missing values Missing
family has 11642 (1.9%) missing values Missing
subfamily has 604624 (> 99.9%) missing values Missing
tribe has 604626 (100.0%) missing values Missing
subtribe has 604624 (> 99.9%) missing values Missing
genus has 19883 (3.3%) missing values Missing
genericName has 19882 (3.3%) missing values Missing
subgenus has 604624 (> 99.9%) missing values Missing
infragenericEpithet has 604626 (100.0%) missing values Missing
specificEpithet has 109508 (18.1%) missing values Missing
infraspecificEpithet has 586367 (97.0%) missing values Missing
cultivarEpithet has 604624 (> 99.9%) missing values Missing
verbatimTaxonRank has 604625 (> 99.9%) missing values Missing
vernacularName has 604624 (> 99.9%) missing values Missing
nomenclaturalCode has 604625 (> 99.9%) missing values Missing
nomenclaturalStatus has 604625 (> 99.9%) missing values Missing
taxonRemarks has 604625 (> 99.9%) missing values Missing
elevation has 557870 (92.3%) missing values Missing
elevationAccuracy has 573282 (94.8%) missing values Missing
depth has 604592 (> 99.9%) missing values Missing
depthAccuracy has 604615 (> 99.9%) missing values Missing
distanceFromCentroidInMeters has 601631 (99.5%) missing values Missing
mediaType has 369838 (61.2%) missing values Missing
familyKey has 11642 (1.9%) missing values Missing
genusKey has 19883 (3.3%) missing values Missing
subgenusKey has 604624 (> 99.9%) missing values Missing
speciesKey has 109501 (18.1%) missing values Missing
species has 109503 (18.1%) missing values Missing
typifiedName has 604626 (100.0%) missing values Missing
repatriated has 162658 (26.9%) missing values Missing
relativeOrganismQuantity has 604626 (100.0%) missing values Missing
projectId has 604625 (> 99.9%) missing values Missing
gbifRegion has 163113 (27.0%) missing values Missing
level0Gid has 288722 (47.8%) missing values Missing
level0Name has 288722 (47.8%) missing values Missing
level1Gid has 288806 (47.8%) missing values Missing
level1Name has 288804 (47.8%) missing values Missing
level2Gid has 297499 (49.2%) missing values Missing
level2Name has 297510 (49.2%) missing values Missing
level3Gid has 540301 (89.4%) missing values Missing
level3Name has 541181 (89.5%) missing values Missing
iucnRedListCategory has 96088 (15.9%) missing values Missing
Unnamed: 223 has 604626 (100.0%) missing values Missing
Unnamed: 224 has 604626 (100.0%) missing values Missing
Unnamed: 225 has 604626 (100.0%) missing values Missing
Unnamed: 226 has 604626 (100.0%) missing values Missing
Unnamed: 227 has 604626 (100.0%) missing values Missing
Unnamed: 228 has 604624 (> 99.9%) missing values Missing
Unnamed: 229 has 604625 (> 99.9%) missing values Missing
Unnamed: 230 has 604624 (> 99.9%) missing values Missing
Unnamed: 231 has 604624 (> 99.9%) missing values Missing
Unnamed: 232 has 604624 (> 99.9%) missing values Missing
Unnamed: 233 has 604624 (> 99.9%) missing values Missing
Unnamed: 234 has 604624 (> 99.9%) missing values Missing
Unnamed: 235 has 604624 (> 99.9%) missing values Missing
Unnamed: 236 has 604624 (> 99.9%) missing values Missing
Unnamed: 237 has 604624 (> 99.9%) missing values Missing
Unnamed: 238 has 604624 (> 99.9%) missing values Missing
Unnamed: 239 has 604624 (> 99.9%) missing values Missing
Unnamed: 240 has 604626 (100.0%) missing values Missing
Unnamed: 241 has 604625 (> 99.9%) missing values Missing
Unnamed: 242 has 604625 (> 99.9%) missing values Missing
Unnamed: 243 has 604624 (> 99.9%) missing values Missing
Unnamed: 244 has 604624 (> 99.9%) missing values Missing
Unnamed: 245 has 604626 (100.0%) missing values Missing
Unnamed: 246 has 604624 (> 99.9%) missing values Missing
Unnamed: 247 has 604624 (> 99.9%) missing values Missing
Unnamed: 248 has 604624 (> 99.9%) missing values Missing
Unnamed: 249 has 604624 (> 99.9%) missing values Missing
Unnamed: 250 has 604626 (100.0%) missing values Missing
Unnamed: 251 has 604626 (100.0%) missing values Missing
Unnamed: 252 has 604624 (> 99.9%) missing values Missing
Unnamed: 253 has 604624 (> 99.9%) missing values Missing
Unnamed: 254 has 604624 (> 99.9%) missing values Missing
Unnamed: 255 has 604624 (> 99.9%) missing values Missing
Unnamed: 256 has 604624 (> 99.9%) missing values Missing
Unnamed: 257 has 604624 (> 99.9%) missing values Missing
Unnamed: 258 has 604624 (> 99.9%) missing values Missing
Unnamed: 259 has 604624 (> 99.9%) missing values Missing
Unnamed: 260 has 604624 (> 99.9%) missing values Missing
Unnamed: 261 has 604625 (> 99.9%) missing values Missing
Unnamed: 262 has 604625 (> 99.9%) missing values Missing
Unnamed: 263 has 604625 (> 99.9%) missing values Missing
individualCount is highly skewed (γ1 = 774.5611586) Skewed
elevationAccuracy is highly skewed (γ1 = 170.1239669) Skewed
phylumKey is highly skewed (γ1 = -111.556886) Skewed
classKey is highly skewed (γ1 = 24.87357061) Skewed
gbifID has unique values Unique
occurrenceID has unique values Unique
accessRights is an unsupported type, check if it needs cleaning or further analysis Unsupported
bibliographicCitation is an unsupported type, check if it needs cleaning or further analysis Unsupported
language is an unsupported type, check if it needs cleaning or further analysis Unsupported
references is an unsupported type, check if it needs cleaning or further analysis Unsupported
rightsHolder is an unsupported type, check if it needs cleaning or further analysis Unsupported
type is an unsupported type, check if it needs cleaning or further analysis Unsupported
datasetID is an unsupported type, check if it needs cleaning or further analysis Unsupported
ownerInstitutionCode is an unsupported type, check if it needs cleaning or further analysis Unsupported
informationWithheld is an unsupported type, check if it needs cleaning or further analysis Unsupported
dataGeneralizations is an unsupported type, check if it needs cleaning or further analysis Unsupported
dynamicProperties is an unsupported type, check if it needs cleaning or further analysis Unsupported
recordNumber is an unsupported type, check if it needs cleaning or further analysis Unsupported
recordedByID is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismQuantity is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismQuantityType is an unsupported type, check if it needs cleaning or further analysis Unsupported
reproductiveCondition is an unsupported type, check if it needs cleaning or further analysis Unsupported
caste is an unsupported type, check if it needs cleaning or further analysis Unsupported
behavior is an unsupported type, check if it needs cleaning or further analysis Unsupported
vitality is an unsupported type, check if it needs cleaning or further analysis Unsupported
establishmentMeans is an unsupported type, check if it needs cleaning or further analysis Unsupported
degreeOfEstablishment is an unsupported type, check if it needs cleaning or further analysis Unsupported
pathway is an unsupported type, check if it needs cleaning or further analysis Unsupported
georeferenceVerificationStatus is an unsupported type, check if it needs cleaning or further analysis Unsupported
disposition is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedOccurrences is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedReferences is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedSequences is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedTaxa is an unsupported type, check if it needs cleaning or further analysis Unsupported
otherCatalogNumbers is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismID is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismName is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismScope is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedOrganisms is an unsupported type, check if it needs cleaning or further analysis Unsupported
previousIdentifications is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
materialEntityID is an unsupported type, check if it needs cleaning or further analysis Unsupported
materialEntityRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventID is an unsupported type, check if it needs cleaning or further analysis Unsupported
parentEventID is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventType is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventTime is an unsupported type, check if it needs cleaning or further analysis Unsupported
samplingProtocol is an unsupported type, check if it needs cleaning or further analysis Unsupported
sampleSizeValue is an unsupported type, check if it needs cleaning or further analysis Unsupported
sampleSizeUnit is an unsupported type, check if it needs cleaning or further analysis Unsupported
samplingEffort is an unsupported type, check if it needs cleaning or further analysis Unsupported
fieldNotes is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
locationID is an unsupported type, check if it needs cleaning or further analysis Unsupported
higherGeographyID is an unsupported type, check if it needs cleaning or further analysis Unsupported
waterBody is an unsupported type, check if it needs cleaning or further analysis Unsupported
municipality is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimLocality is an unsupported type, check if it needs cleaning or further analysis Unsupported
verticalDatum is an unsupported type, check if it needs cleaning or further analysis Unsupported
maximumDistanceAboveSurfaceInMeters is an unsupported type, check if it needs cleaning or further analysis Unsupported
locationAccordingTo is an unsupported type, check if it needs cleaning or further analysis Unsupported
locationRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
coordinatePrecision is an unsupported type, check if it needs cleaning or further analysis Unsupported
footprintWKT is an unsupported type, check if it needs cleaning or further analysis Unsupported
georeferencedBy is an unsupported type, check if it needs cleaning or further analysis Unsupported
geologicalContextID is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestEonOrLowestEonothem is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestEpochOrLowestSeries is an unsupported type, check if it needs cleaning or further analysis Unsupported
latestAgeOrHighestStage is an unsupported type, check if it needs cleaning or further analysis Unsupported
lowestBiostratigraphicZone is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationID is an unsupported type, check if it needs cleaning or further analysis Unsupported
dateIdentified is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationReferences is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationVerificationStatus is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
scientificNameID is an unsupported type, check if it needs cleaning or further analysis Unsupported
parentNameUsageID is an unsupported type, check if it needs cleaning or further analysis Unsupported
originalNameUsageID is an unsupported type, check if it needs cleaning or further analysis Unsupported
nameAccordingToID is an unsupported type, check if it needs cleaning or further analysis Unsupported
parentNameUsage is an unsupported type, check if it needs cleaning or further analysis Unsupported
tribe is an unsupported type, check if it needs cleaning or further analysis Unsupported
infragenericEpithet is an unsupported type, check if it needs cleaning or further analysis Unsupported
hasCoordinate is an unsupported type, check if it needs cleaning or further analysis Unsupported
orderKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
familyKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
genusKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
speciesKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
typifiedName is an unsupported type, check if it needs cleaning or further analysis Unsupported
relativeOrganismQuantity is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 223 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 224 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 225 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 226 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 227 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 240 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 245 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 250 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 251 is an unsupported type, check if it needs cleaning or further analysis Unsupported
elevationAccuracy has 27237 (4.5%) zeros Zeros

Reproduction

Analysis started2025-01-07 15:41:02.888161
Analysis finished2025-01-07 15:41:34.498206
Duration31.61 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

gbifID
Real number (ℝ)

Unique 

Distinct604626
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1756430344
Minimum1317202463
Maximum4987328294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:34.805220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1317202463
5-th percentile1317610202
Q11319213098
median1321227831
Q31675914307
95-th percentile4403904310
Maximum4987328294
Range3670125831
Interquartile range (IQR)356701209.8

Descriptive statistics

Standard deviation879774786.1
Coefficient of variation (CV)0.5008879453
Kurtosis3.454581398
Mean1756430344
Median Absolute Deviation (MAD)2269720.5
Skewness2.100719268
Sum1.061983453 × 1015
Variance7.740036742 × 1017
MonotonicityNot monotonic
2025-01-07T10:41:34.869906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1319867829 1
 
< 0.1%
1321729650 1
 
< 0.1%
1319851592 1
 
< 0.1%
1319850657 1
 
< 0.1%
1318325546 1
 
< 0.1%
1318325316 1
 
< 0.1%
3026594307 1
 
< 0.1%
1318324785 1
 
< 0.1%
1318324318 1
 
< 0.1%
1319848254 1
 
< 0.1%
Other values (604616) 604616
> 99.9%
ValueCountFrequency (%)
1317202463 1
< 0.1%
1317202478 1
< 0.1%
1317202488 1
< 0.1%
1317202492 1
< 0.1%
1317202508 1
< 0.1%
ValueCountFrequency (%)
4987328294 1
< 0.1%
4987328292 1
< 0.1%
4987328289 1
< 0.1%
4987328288 1
< 0.1%
4987328287 1
< 0.1%

accessRights
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

bibliographicCitation
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

language
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

license
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:34.958757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters4232382
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCC0_1_0
2nd rowCC0_1_0
3rd rowCC0_1_0
4th rowCC0_1_0
5th rowCC0_1_0
ValueCountFrequency (%)
cc0_1_0 604626
100.0%
2025-01-07T10:41:35.198106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 1209252
28.6%
0 1209252
28.6%
_ 1209252
28.6%
1 604626
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4232382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1209252
28.6%
0 1209252
28.6%
_ 1209252
28.6%
1 604626
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4232382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1209252
28.6%
0 1209252
28.6%
_ 1209252
28.6%
1 604626
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4232382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1209252
28.6%
0 1209252
28.6%
_ 1209252
28.6%
1 604626
14.3%
Distinct56588
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:35.331044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters12092520
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30778 ?
Unique (%)5.1%

Sample

1st row2013-09-16T11:56:00Z
2nd row2016-06-09T14:33:00Z
3rd row2023-08-23T09:36:00Z
4th row2023-05-19T10:32:00Z
5th row2015-10-05T15:58:00Z
ValueCountFrequency (%)
2017-04-17t11:48:00z 9681
 
1.6%
2017-04-17t11:49:00z 9420
 
1.6%
2017-04-17t11:50:00z 8719
 
1.4%
2017-04-17t11:47:00z 8654
 
1.4%
2017-04-17t11:46:00z 6000
 
1.0%
2021-08-23t15:49:00z 3095
 
0.5%
2021-08-23t15:48:00z 3057
 
0.5%
2016-07-27t14:05:00z 3041
 
0.5%
2016-07-27t14:06:00z 1844
 
0.3%
2021-08-23t15:50:00z 1737
 
0.3%
Other values (56578) 549378
90.9%
2025-01-07T10:41:35.525460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2927853
24.2%
1 1566143
13.0%
2 1372338
11.3%
- 1209252
10.0%
: 1209252
10.0%
T 604626
 
5.0%
Z 604626
 
5.0%
3 593038
 
4.9%
5 494587
 
4.1%
4 456514
 
3.8%
Other values (4) 1054291
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12092520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2927853
24.2%
1 1566143
13.0%
2 1372338
11.3%
- 1209252
10.0%
: 1209252
10.0%
T 604626
 
5.0%
Z 604626
 
5.0%
3 593038
 
4.9%
5 494587
 
4.1%
4 456514
 
3.8%
Other values (4) 1054291
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12092520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2927853
24.2%
1 1566143
13.0%
2 1372338
11.3%
- 1209252
10.0%
: 1209252
10.0%
T 604626
 
5.0%
Z 604626
 
5.0%
3 593038
 
4.9%
5 494587
 
4.1%
4 456514
 
3.8%
Other values (4) 1054291
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12092520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2927853
24.2%
1 1566143
13.0%
2 1372338
11.3%
- 1209252
10.0%
: 1209252
10.0%
T 604626
 
5.0%
Z 604626
 
5.0%
3 593038
 
4.9%
5 494587
 
4.1%
4 456514
 
3.8%
Other values (4) 1054291
 
8.7%

publisher
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:35.594435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length59
Median length59
Mean length59
Min length59

Characters and Unicode

Total characters35672934
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational Museum of Natural History, Smithsonian Institution
2nd rowNational Museum of Natural History, Smithsonian Institution
3rd rowNational Museum of Natural History, Smithsonian Institution
4th rowNational Museum of Natural History, Smithsonian Institution
5th rowNational Museum of Natural History, Smithsonian Institution
ValueCountFrequency (%)
national 604626
14.3%
museum 604626
14.3%
of 604626
14.3%
natural 604626
14.3%
history 604626
14.3%
smithsonian 604626
14.3%
institution 604626
14.3%
2025-01-07T10:41:35.705646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 4232382
11.9%
i 3627756
10.2%
3627756
10.2%
o 3023130
 
8.5%
a 3023130
 
8.5%
n 3023130
 
8.5%
s 2418504
 
6.8%
u 2418504
 
6.8%
N 1209252
 
3.4%
m 1209252
 
3.4%
Other values (11) 7860138
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35672934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 4232382
11.9%
i 3627756
10.2%
3627756
10.2%
o 3023130
 
8.5%
a 3023130
 
8.5%
n 3023130
 
8.5%
s 2418504
 
6.8%
u 2418504
 
6.8%
N 1209252
 
3.4%
m 1209252
 
3.4%
Other values (11) 7860138
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35672934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 4232382
11.9%
i 3627756
10.2%
3627756
10.2%
o 3023130
 
8.5%
a 3023130
 
8.5%
n 3023130
 
8.5%
s 2418504
 
6.8%
u 2418504
 
6.8%
N 1209252
 
3.4%
m 1209252
 
3.4%
Other values (11) 7860138
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35672934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 4232382
11.9%
i 3627756
10.2%
3627756
10.2%
o 3023130
 
8.5%
a 3023130
 
8.5%
n 3023130
 
8.5%
s 2418504
 
6.8%
u 2418504
 
6.8%
N 1209252
 
3.4%
m 1209252
 
3.4%
Other values (11) 7860138
22.0%

references
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

rightsHolder
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

type
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

institutionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:35.760645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters17534154
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:lsid:biocol.org:col:34871
2nd rowurn:lsid:biocol.org:col:34871
3rd rowurn:lsid:biocol.org:col:34871
4th rowurn:lsid:biocol.org:col:34871
5th rowurn:lsid:biocol.org:col:34871
ValueCountFrequency (%)
urn:lsid:biocol.org:col:34871 604626
100.0%
2025-01-07T10:41:35.862395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 2418504
13.8%
: 2418504
13.8%
l 1813878
 
10.3%
r 1209252
 
6.9%
c 1209252
 
6.9%
i 1209252
 
6.9%
u 604626
 
3.4%
s 604626
 
3.4%
d 604626
 
3.4%
n 604626
 
3.4%
Other values (8) 4837008
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17534154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2418504
13.8%
: 2418504
13.8%
l 1813878
 
10.3%
r 1209252
 
6.9%
c 1209252
 
6.9%
i 1209252
 
6.9%
u 604626
 
3.4%
s 604626
 
3.4%
d 604626
 
3.4%
n 604626
 
3.4%
Other values (8) 4837008
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17534154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2418504
13.8%
: 2418504
13.8%
l 1813878
 
10.3%
r 1209252
 
6.9%
c 1209252
 
6.9%
i 1209252
 
6.9%
u 604626
 
3.4%
s 604626
 
3.4%
d 604626
 
3.4%
n 604626
 
3.4%
Other values (8) 4837008
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17534154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2418504
13.8%
: 2418504
13.8%
l 1813878
 
10.3%
r 1209252
 
6.9%
c 1209252
 
6.9%
i 1209252
 
6.9%
u 604626
 
3.4%
s 604626
 
3.4%
d 604626
 
3.4%
n 604626
 
3.4%
Other values (8) 4837008
27.6%

collectionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:35.917290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length45
Mean length45
Min length45

Characters and Unicode

Total characters27208170
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:uuid:18e3cd08-a962-4f0a-b72c-9a0b3600c5ad
2nd rowurn:uuid:18e3cd08-a962-4f0a-b72c-9a0b3600c5ad
3rd rowurn:uuid:18e3cd08-a962-4f0a-b72c-9a0b3600c5ad
4th rowurn:uuid:18e3cd08-a962-4f0a-b72c-9a0b3600c5ad
5th rowurn:uuid:18e3cd08-a962-4f0a-b72c-9a0b3600c5ad
ValueCountFrequency (%)
urn:uuid:18e3cd08-a962-4f0a-b72c-9a0b3600c5ad 604626
100.0%
2025-01-07T10:41:36.024249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3023130
 
11.1%
- 2418504
 
8.9%
a 2418504
 
8.9%
c 1813878
 
6.7%
u 1813878
 
6.7%
d 1813878
 
6.7%
2 1209252
 
4.4%
b 1209252
 
4.4%
6 1209252
 
4.4%
: 1209252
 
4.4%
Other values (12) 9069390
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27208170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3023130
 
11.1%
- 2418504
 
8.9%
a 2418504
 
8.9%
c 1813878
 
6.7%
u 1813878
 
6.7%
d 1813878
 
6.7%
2 1209252
 
4.4%
b 1209252
 
4.4%
6 1209252
 
4.4%
: 1209252
 
4.4%
Other values (12) 9069390
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27208170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3023130
 
11.1%
- 2418504
 
8.9%
a 2418504
 
8.9%
c 1813878
 
6.7%
u 1813878
 
6.7%
d 1813878
 
6.7%
2 1209252
 
4.4%
b 1209252
 
4.4%
6 1209252
 
4.4%
: 1209252
 
4.4%
Other values (12) 9069390
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27208170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3023130
 
11.1%
- 2418504
 
8.9%
a 2418504
 
8.9%
c 1813878
 
6.7%
u 1813878
 
6.7%
d 1813878
 
6.7%
2 1209252
 
4.4%
b 1209252
 
4.4%
6 1209252
 
4.4%
: 1209252
 
4.4%
Other values (12) 9069390
33.3%

datasetID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:36.063247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2418504
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSNM
2nd rowUSNM
3rd rowUSNM
4th rowUSNM
5th rowUSNM
ValueCountFrequency (%)
usnm 604626
100.0%
2025-01-07T10:41:36.154668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 604626
25.0%
S 604626
25.0%
N 604626
25.0%
M 604626
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2418504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 604626
25.0%
S 604626
25.0%
N 604626
25.0%
M 604626
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2418504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 604626
25.0%
S 604626
25.0%
N 604626
25.0%
M 604626
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2418504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 604626
25.0%
S 604626
25.0%
N 604626
25.0%
M 604626
25.0%

collectionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:36.193475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1813878
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENT
2nd rowENT
3rd rowENT
4th rowENT
5th rowENT
ValueCountFrequency (%)
ent 604626
100.0%
2025-01-07T10:41:36.284281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 604626
33.3%
N 604626
33.3%
T 604626
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1813878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 604626
33.3%
N 604626
33.3%
T 604626
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1813878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 604626
33.3%
N 604626
33.3%
T 604626
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1813878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 604626
33.3%
N 604626
33.3%
T 604626
33.3%

datasetName
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:36.326794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters11487894
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNMNH Extant Biology
2nd rowNMNH Extant Biology
3rd rowNMNH Extant Biology
4th rowNMNH Extant Biology
5th rowNMNH Extant Biology
ValueCountFrequency (%)
nmnh 604626
33.3%
extant 604626
33.3%
biology 604626
33.3%
2025-01-07T10:41:36.420764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1209252
 
10.5%
t 1209252
 
10.5%
1209252
 
10.5%
o 1209252
 
10.5%
H 604626
 
5.3%
E 604626
 
5.3%
M 604626
 
5.3%
x 604626
 
5.3%
a 604626
 
5.3%
B 604626
 
5.3%
Other values (5) 3023130
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11487894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1209252
 
10.5%
t 1209252
 
10.5%
1209252
 
10.5%
o 1209252
 
10.5%
H 604626
 
5.3%
E 604626
 
5.3%
M 604626
 
5.3%
x 604626
 
5.3%
a 604626
 
5.3%
B 604626
 
5.3%
Other values (5) 3023130
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11487894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1209252
 
10.5%
t 1209252
 
10.5%
1209252
 
10.5%
o 1209252
 
10.5%
H 604626
 
5.3%
E 604626
 
5.3%
M 604626
 
5.3%
x 604626
 
5.3%
a 604626
 
5.3%
B 604626
 
5.3%
Other values (5) 3023130
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11487894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1209252
 
10.5%
t 1209252
 
10.5%
1209252
 
10.5%
o 1209252
 
10.5%
H 604626
 
5.3%
E 604626
 
5.3%
M 604626
 
5.3%
x 604626
 
5.3%
a 604626
 
5.3%
B 604626
 
5.3%
Other values (5) 3023130
26.3%

ownerInstitutionCode
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:36.471813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.99374986
Min length17

Characters and Unicode

Total characters10879489
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRESERVED_SPECIMEN
2nd rowPRESERVED_SPECIMEN
3rd rowPRESERVED_SPECIMEN
4th rowPRESERVED_SPECIMEN
5th rowPRESERVED_SPECIMEN
ValueCountFrequency (%)
preserved_specimen 600847
99.4%
human_observation 3779
 
0.6%
2025-01-07T10:41:36.588892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3008014
27.6%
S 1205473
11.1%
R 1205473
11.1%
P 1201694
 
11.0%
N 608405
 
5.6%
_ 604626
 
5.6%
I 604626
 
5.6%
M 604626
 
5.6%
V 604626
 
5.6%
C 600847
 
5.5%
Other values (7) 631079
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10879489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 3008014
27.6%
S 1205473
11.1%
R 1205473
11.1%
P 1201694
 
11.0%
N 608405
 
5.6%
_ 604626
 
5.6%
I 604626
 
5.6%
M 604626
 
5.6%
V 604626
 
5.6%
C 600847
 
5.5%
Other values (7) 631079
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10879489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 3008014
27.6%
S 1205473
11.1%
R 1205473
11.1%
P 1201694
 
11.0%
N 608405
 
5.6%
_ 604626
 
5.6%
I 604626
 
5.6%
M 604626
 
5.6%
V 604626
 
5.6%
C 600847
 
5.5%
Other values (7) 631079
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10879489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 3008014
27.6%
S 1205473
11.1%
R 1205473
11.1%
P 1201694
 
11.0%
N 608405
 
5.6%
_ 604626
 
5.6%
I 604626
 
5.6%
M 604626
 
5.6%
V 604626
 
5.6%
C 600847
 
5.5%
Other values (7) 631079
 
5.8%

informationWithheld
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

dataGeneralizations
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

dynamicProperties
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

occurrenceID
Text

Unique 

Distinct604626
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:36.899111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length63
Mean length63
Min length63

Characters and Unicode

Total characters38091438
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique604626 ?
Unique (%)100.0%

Sample

1st rowhttp://n2t.net/ark:/65665/3c83a10d1-1e59-4b08-af5b-28d12d2d0c80
2nd rowhttp://n2t.net/ark:/65665/383bb510d-d5ce-4c09-b4c4-bc1482fbaf28
3rd rowhttp://n2t.net/ark:/65665/383f13aa6-a5b6-40bc-bddc-b42c557aebfc
4th rowhttp://n2t.net/ark:/65665/383f4d560-c2d2-485c-906c-b6dad303fd7a
5th rowhttp://n2t.net/ark:/65665/383f634da-bb58-423c-85f4-a267b04c5ee5
ValueCountFrequency (%)
http://n2t.net/ark:/65665/383f13aa6-a5b6-40bc-bddc-b42c557aebfc 1
 
< 0.1%
http://n2t.net/ark:/65665/375e78f71-1b00-4c3f-8fe2-561bc886bf57 1
 
< 0.1%
http://n2t.net/ark:/65665/37536c9c0-1435-4a3b-91f9-71630cc6301d 1
 
< 0.1%
http://n2t.net/ark:/65665/331c51385-a00b-4b6e-b5c0-4121d37292fa 1
 
< 0.1%
http://n2t.net/ark:/65665/331ecf480-3b28-404d-b1d1-4cbddbeb52bc 1
 
< 0.1%
http://n2t.net/ark:/65665/3756722c4-efb0-4916-b840-7d1456d176de 1
 
< 0.1%
http://n2t.net/ark:/65665/375727131-1abf-40a6-941c-6e756318b8d1 1
 
< 0.1%
http://n2t.net/ark:/65665/3758141d1-aadb-4e10-a1bb-682b5ee458b6 1
 
< 0.1%
http://n2t.net/ark:/65665/33212a8be-ff30-4c90-bb62-c52178ed1293 1
 
< 0.1%
http://n2t.net/ark:/65665/332324e29-f306-495f-95ba-ae307fe7a198 1
 
< 0.1%
Other values (604616) 604616
> 99.9%
2025-01-07T10:41:37.271199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 3023130
 
7.9%
6 2949272
 
7.7%
- 2418504
 
6.3%
t 2418504
 
6.3%
5 2343156
 
6.2%
a 1889243
 
5.0%
2 1738952
 
4.6%
e 1738278
 
4.6%
3 1737371
 
4.6%
4 1737249
 
4.6%
Other values (16) 16097779
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38091438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 3023130
 
7.9%
6 2949272
 
7.7%
- 2418504
 
6.3%
t 2418504
 
6.3%
5 2343156
 
6.2%
a 1889243
 
5.0%
2 1738952
 
4.6%
e 1738278
 
4.6%
3 1737371
 
4.6%
4 1737249
 
4.6%
Other values (16) 16097779
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38091438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 3023130
 
7.9%
6 2949272
 
7.7%
- 2418504
 
6.3%
t 2418504
 
6.3%
5 2343156
 
6.2%
a 1889243
 
5.0%
2 1738952
 
4.6%
e 1738278
 
4.6%
3 1737371
 
4.6%
4 1737249
 
4.6%
Other values (16) 16097779
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38091438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 3023130
 
7.9%
6 2949272
 
7.7%
- 2418504
 
6.3%
t 2418504
 
6.3%
5 2343156
 
6.2%
a 1889243
 
5.0%
2 1738952
 
4.6%
e 1738278
 
4.6%
3 1737371
 
4.6%
4 1737249
 
4.6%
Other values (16) 16097779
42.3%

catalogNumber
Text

Missing 

Distinct371195
Distinct (%)> 99.9%
Missing233418
Missing (%)38.6%
Memory size4.6 MiB
2025-01-07T10:41:37.541977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length15
Mean length15.03873031
Min length12

Characters and Unicode

Total characters5582497
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique371182 ?
Unique (%)> 99.9%

Sample

1st rowUSNMENT00831303
2nd rowUSNMENT00356408
3rd rowUSNMENT01436172
4th rowUSNMENT00357025
5th rowUSNMENT00314717
ValueCountFrequency (%)
usnment00377587 2
 
< 0.1%
usnment00935890 2
 
< 0.1%
usnment00937222 2
 
< 0.1%
usnment00385557 2
 
< 0.1%
usnment00533165 2
 
< 0.1%
usnment00937212 2
 
< 0.1%
usnment00536541 2
 
< 0.1%
usnment00385731 2
 
< 0.1%
usnment00937214 2
 
< 0.1%
usnment00377617 2
 
< 0.1%
Other values (371185) 371188
> 99.9%
2025-01-07T10:41:37.873094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 804605
14.4%
N 741758
13.3%
1 376970
 
6.8%
S 371208
 
6.6%
U 371164
 
6.6%
M 371164
 
6.6%
E 370588
 
6.6%
T 370588
 
6.6%
3 302793
 
5.4%
4 225899
 
4.0%
Other values (11) 1275760
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5582497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 804605
14.4%
N 741758
13.3%
1 376970
 
6.8%
S 371208
 
6.6%
U 371164
 
6.6%
M 371164
 
6.6%
E 370588
 
6.6%
T 370588
 
6.6%
3 302793
 
5.4%
4 225899
 
4.0%
Other values (11) 1275760
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5582497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 804605
14.4%
N 741758
13.3%
1 376970
 
6.8%
S 371208
 
6.6%
U 371164
 
6.6%
M 371164
 
6.6%
E 370588
 
6.6%
T 370588
 
6.6%
3 302793
 
5.4%
4 225899
 
4.0%
Other values (11) 1275760
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5582497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 804605
14.4%
N 741758
13.3%
1 376970
 
6.8%
S 371208
 
6.6%
U 371164
 
6.6%
M 371164
 
6.6%
E 370588
 
6.6%
T 370588
 
6.6%
3 302793
 
5.4%
4 225899
 
4.0%
Other values (11) 1275760
22.9%

recordNumber
Unsupported

Missing  Rejected  Unsupported 

Missing604589
Missing (%)> 99.9%
Memory size4.6 MiB

recordedBy
Text

Missing 

Distinct18726
Distinct (%)4.7%
Missing203336
Missing (%)33.6%
Memory size4.6 MiB
2025-01-07T10:41:38.068084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length90
Median length84
Mean length11.25684667
Min length1

Characters and Unicode

Total characters4517260
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9104 ?
Unique (%)2.3%

Sample

1st rowM. Ortiz B.
2nd row[Not Stated]
3rd rowS. Roble
4th row[Not Stated]
5th rowC. Flint
ValueCountFrequency (%)
not 65711
 
7.2%
stated 65695
 
7.2%
l 40182
 
4.4%
39875
 
4.4%
j 36886
 
4.0%
macior 31232
 
3.4%
d 28468
 
3.1%
c 27156
 
3.0%
r 25636
 
2.8%
b 22044
 
2.4%
Other values (10691) 530776
58.1%
2025-01-07T10:41:38.336658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
512371
 
11.3%
. 355530
 
7.9%
t 305132
 
6.8%
a 299337
 
6.6%
e 290066
 
6.4%
o 240179
 
5.3%
r 229270
 
5.1%
i 173763
 
3.8%
n 169850
 
3.8%
l 136863
 
3.0%
Other values (73) 1804899
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4517260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
512371
 
11.3%
. 355530
 
7.9%
t 305132
 
6.8%
a 299337
 
6.6%
e 290066
 
6.4%
o 240179
 
5.3%
r 229270
 
5.1%
i 173763
 
3.8%
n 169850
 
3.8%
l 136863
 
3.0%
Other values (73) 1804899
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4517260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
512371
 
11.3%
. 355530
 
7.9%
t 305132
 
6.8%
a 299337
 
6.6%
e 290066
 
6.4%
o 240179
 
5.3%
r 229270
 
5.1%
i 173763
 
3.8%
n 169850
 
3.8%
l 136863
 
3.0%
Other values (73) 1804899
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4517260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
512371
 
11.3%
. 355530
 
7.9%
t 305132
 
6.8%
a 299337
 
6.6%
e 290066
 
6.4%
o 240179
 
5.3%
r 229270
 
5.1%
i 173763
 
3.8%
n 169850
 
3.8%
l 136863
 
3.0%
Other values (73) 1804899
40.0%

recordedByID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

individualCount
Real number (ℝ)

Skewed 

Distinct941
Distinct (%)0.2%
Missing3136
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean22.77984671
Minimum0
Maximum9989248
Zeros109
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:38.413525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum9989248
Range9989248
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12885.66064
Coefficient of variation (CV)565.6605507
Kurtosis600447.5571
Mean22.77984671
Median Absolute Deviation (MAD)0
Skewness774.5611586
Sum13701850
Variance166040250.1
MonotonicityNot monotonic
2025-01-07T10:41:38.478477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 548219
90.7%
2 10272
 
1.7%
3 6617
 
1.1%
4 4294
 
0.7%
5 2621
 
0.4%
6 2340
 
0.4%
7 1822
 
0.3%
8 1526
 
0.3%
10 1306
 
0.2%
9 1254
 
0.2%
Other values (931) 21219
 
3.5%
(Missing) 3136
 
0.5%
ValueCountFrequency (%)
0 109
 
< 0.1%
1 548219
90.7%
2 10272
 
1.7%
3 6617
 
1.1%
4 4294
 
0.7%
ValueCountFrequency (%)
9989248 1
< 0.1%
239234 1
< 0.1%
87081 1
< 0.1%
59330 1
< 0.1%
56459 1
< 0.1%

organismQuantity
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

organismQuantityType
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

sex
Text

Missing 

Distinct2
Distinct (%)< 0.1%
Missing384462
Missing (%)63.6%
Memory size4.6 MiB
2025-01-07T10:41:38.518458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.79924965
Min length4

Characters and Unicode

Total characters1056622
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
4th rowMALE
5th rowFEMALE
ValueCountFrequency (%)
male 132181
60.0%
female 87983
40.0%
2025-01-07T10:41:38.701841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 308147
29.2%
M 220164
20.8%
A 220164
20.8%
L 220164
20.8%
F 87983
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1056622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 308147
29.2%
M 220164
20.8%
A 220164
20.8%
L 220164
20.8%
F 87983
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1056622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 308147
29.2%
M 220164
20.8%
A 220164
20.8%
L 220164
20.8%
F 87983
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1056622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 308147
29.2%
M 220164
20.8%
A 220164
20.8%
L 220164
20.8%
F 87983
 
8.3%

lifeStage
Text

Missing 

Distinct10
Distinct (%)< 0.1%
Missing184129
Missing (%)30.5%
Memory size4.6 MiB
2025-01-07T10:41:38.748887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.02011905
Min length3

Characters and Unicode

Total characters2110945
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAdult
2nd rowAdult
3rd rowAdult
4th rowAdult
5th rowAdult
ValueCountFrequency (%)
adult 415182
98.7%
immature 2800
 
0.7%
pupa 946
 
0.2%
larva 886
 
0.2%
unknown 490
 
0.1%
nymph 139
 
< 0.1%
egg 34
 
< 0.1%
deutonymph 17
 
< 0.1%
juvenile 2
 
< 0.1%
subadult 1
 
< 0.1%
2025-01-07T10:41:38.850713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u 418949
19.8%
t 418000
19.8%
l 415185
19.7%
d 415183
19.7%
A 415182
19.7%
m 5756
 
0.3%
a 5519
 
0.3%
r 3686
 
0.2%
e 2821
 
0.1%
I 2800
 
0.1%
Other values (19) 7864
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2110945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 418949
19.8%
t 418000
19.8%
l 415185
19.7%
d 415183
19.7%
A 415182
19.7%
m 5756
 
0.3%
a 5519
 
0.3%
r 3686
 
0.2%
e 2821
 
0.1%
I 2800
 
0.1%
Other values (19) 7864
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2110945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 418949
19.8%
t 418000
19.8%
l 415185
19.7%
d 415183
19.7%
A 415182
19.7%
m 5756
 
0.3%
a 5519
 
0.3%
r 3686
 
0.2%
e 2821
 
0.1%
I 2800
 
0.1%
Other values (19) 7864
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2110945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 418949
19.8%
t 418000
19.8%
l 415185
19.7%
d 415183
19.7%
A 415182
19.7%
m 5756
 
0.3%
a 5519
 
0.3%
r 3686
 
0.2%
e 2821
 
0.1%
I 2800
 
0.1%
Other values (19) 7864
 
0.4%

reproductiveCondition
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

caste
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

behavior
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

vitality
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

establishmentMeans
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

degreeOfEstablishment
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

pathway
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

georeferenceVerificationStatus
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

occurrenceStatus
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:38.894799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters4232382
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRESENT
2nd rowPRESENT
3rd rowPRESENT
4th rowPRESENT
5th rowPRESENT
ValueCountFrequency (%)
present 604626
100.0%
2025-01-07T10:41:38.993220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1209252
28.6%
P 604626
14.3%
R 604626
14.3%
S 604626
14.3%
N 604626
14.3%
T 604626
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4232382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1209252
28.6%
P 604626
14.3%
R 604626
14.3%
S 604626
14.3%
N 604626
14.3%
T 604626
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4232382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1209252
28.6%
P 604626
14.3%
R 604626
14.3%
S 604626
14.3%
N 604626
14.3%
T 604626
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4232382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1209252
28.6%
P 604626
14.3%
R 604626
14.3%
S 604626
14.3%
N 604626
14.3%
T 604626
14.3%

preparations
Text

Missing 

Distinct272
Distinct (%)< 0.1%
Missing42051
Missing (%)7.0%
Memory size4.6 MiB
2025-01-07T10:41:39.054647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length93
Median length6
Mean length6.839850687
Min length1

Characters and Unicode

Total characters3847929
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112 ?
Unique (%)< 0.1%

Sample

1st rowPinned
2nd rowPinned
3rd rowPinned
4th rowEnvelope
5th rowPinned
ValueCountFrequency (%)
pinned 389733
63.9%
envelope 114672
 
18.8%
slide 65056
 
10.7%
vial 9495
 
1.6%
ethanol 6481
 
1.1%
section 3746
 
0.6%
on 3653
 
0.6%
3195
 
0.5%
ink 3151
 
0.5%
pen 3072
 
0.5%
Other values (93) 7800
 
1.3%
2025-01-07T10:41:39.181789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 916431
23.8%
e 701114
18.2%
i 472644
12.3%
d 455886
11.8%
P 366191
 
9.5%
l 199752
 
5.2%
p 142785
 
3.7%
o 133876
 
3.5%
v 114834
 
3.0%
E 112885
 
2.9%
Other values (48) 231531
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3847929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 916431
23.8%
e 701114
18.2%
i 472644
12.3%
d 455886
11.8%
P 366191
 
9.5%
l 199752
 
5.2%
p 142785
 
3.7%
o 133876
 
3.5%
v 114834
 
3.0%
E 112885
 
2.9%
Other values (48) 231531
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3847929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 916431
23.8%
e 701114
18.2%
i 472644
12.3%
d 455886
11.8%
P 366191
 
9.5%
l 199752
 
5.2%
p 142785
 
3.7%
o 133876
 
3.5%
v 114834
 
3.0%
E 112885
 
2.9%
Other values (48) 231531
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3847929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 916431
23.8%
e 701114
18.2%
i 472644
12.3%
d 455886
11.8%
P 366191
 
9.5%
l 199752
 
5.2%
p 142785
 
3.7%
o 133876
 
3.5%
v 114834
 
3.0%
E 112885
 
2.9%
Other values (48) 231531
 
6.0%

disposition
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

associatedOccurrences
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

associatedReferences
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

associatedSequences
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

associatedTaxa
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

otherCatalogNumbers
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

occurrenceRemarks
Text

Missing 

Distinct31232
Distinct (%)21.5%
Missing459276
Missing (%)76.0%
Memory size4.6 MiB
2025-01-07T10:41:39.363348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length367359
Median length152440
Mean length80.11453732
Min length1

Characters and Unicode

Total characters11644648
Distinct characters126
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27501 ?
Unique (%)18.9%

Sample

1st rowOne leg removed for genetic sampling while on loan to GUELPH.
2nd rowLindroth, 1975:125: (the loc. is no doubt wrong).
3rd rowF. Monros Coll. 1959 G.M. Greene Coll. C. Schaeffer Coll. Shoemaker Coll. 1956 A. Nicolay Coll. 1950 L.W. Saylor Coll.
4th rowSpecimen data is incomplete. Phase 1 of data capture inlcluded USNMENT#s and general locality.
5th rowOne leg removed for genetic sampling while on loan to GUELPH.
ValueCountFrequency (%)
digitization 56218
 
3.3%
by 48162
 
2.8%
digital 44075
 
2.6%
volunteers 44039
 
2.6%
transcribed 44039
 
2.6%
of 43241
 
2.6%
on 41034
 
2.4%
to 36796
 
2.2%
loan 36495
 
2.2%
for 36258
 
2.1%
Other values (49844) 1263433
74.6%
2025-01-07T10:41:39.625464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1504787
 
12.9%
e 838841
 
7.2%
i 811548
 
7.0%
a 687048
 
5.9%
t 675294
 
5.8%
o 659287
 
5.7%
n 620298
 
5.3%
r 558541
 
4.8%
s 454981
 
3.9%
l 435458
 
3.7%
Other values (116) 4398565
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11644648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1504787
 
12.9%
e 838841
 
7.2%
i 811548
 
7.0%
a 687048
 
5.9%
t 675294
 
5.8%
o 659287
 
5.7%
n 620298
 
5.3%
r 558541
 
4.8%
s 454981
 
3.9%
l 435458
 
3.7%
Other values (116) 4398565
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11644648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1504787
 
12.9%
e 838841
 
7.2%
i 811548
 
7.0%
a 687048
 
5.9%
t 675294
 
5.8%
o 659287
 
5.7%
n 620298
 
5.3%
r 558541
 
4.8%
s 454981
 
3.9%
l 435458
 
3.7%
Other values (116) 4398565
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11644648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1504787
 
12.9%
e 838841
 
7.2%
i 811548
 
7.0%
a 687048
 
5.9%
t 675294
 
5.8%
o 659287
 
5.7%
n 620298
 
5.3%
r 558541
 
4.8%
s 454981
 
3.9%
l 435458
 
3.7%
Other values (116) 4398565
37.8%

organismID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

organismName
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

organismScope
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

associatedOrganisms
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

previousIdentifications
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

organismRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

materialEntityID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

materialEntityRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

verbatimLabel
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean-11.7815
Minimum-11.7815
Maximum-11.7815
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:41:39.689219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-11.7815
5-th percentile-11.7815
Q1-11.7815
median-11.7815
Q3-11.7815
95-th percentile-11.7815
Maximum-11.7815
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean-11.7815
Median Absolute Deviation (MAD)0
Skewnessnan
Sum-11.7815
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:41:39.734728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
-11.7815 1
 
< 0.1%
(Missing) 604625
> 99.9%
ValueCountFrequency (%)
-11.7815 1
< 0.1%
ValueCountFrequency (%)
-11.7815 1
< 0.1%

materialSampleID
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean-76.7017
Minimum-76.7017
Maximum-76.7017
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:41:39.777728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-76.7017
5-th percentile-76.7017
Q1-76.7017
median-76.7017
Q3-76.7017
95-th percentile-76.7017
Maximum-76.7017
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean-76.7017
Median Absolute Deviation (MAD)0
Skewnessnan
Sum-76.7017
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:41:39.826097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
-76.7017 1
 
< 0.1%
(Missing) 604625
> 99.9%
ValueCountFrequency (%)
-76.7017 1
< 0.1%
ValueCountFrequency (%)
-76.7017 1
< 0.1%

eventID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

parentEventID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

eventType
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

fieldNumber
Text

Missing 

Distinct3091
Distinct (%)72.7%
Missing600377
Missing (%)99.3%
Memory size4.6 MiB
2025-01-07T10:41:39.981503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length29
Mean length9.591433278
Min length1

Characters and Unicode

Total characters40754
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2646 ?
Unique (%)62.3%

Sample

1st rowBBB991
2nd rowBBB642-DERM
3rd row1653
4th rowJSL021109-18
5th rowCOL-8-101
ValueCountFrequency (%)
1653 128
 
2.8%
2 46
 
1.0%
bbb899-hym 34
 
0.7%
1 32
 
0.7%
bbb791-hym 25
 
0.5%
bbb749-hym 23
 
0.5%
759-8 22
 
0.5%
tub 20
 
0.4%
olym-net-11 18
 
0.4%
bbb638-hym 18
 
0.4%
Other values (3087) 4225
92.0%
2025-01-07T10:41:40.225628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 4781
 
11.7%
0 3995
 
9.8%
- 3976
 
9.8%
1 3398
 
8.3%
2 2238
 
5.5%
3 1558
 
3.8%
6 1541
 
3.8%
7 1509
 
3.7%
4 1498
 
3.7%
9 1481
 
3.6%
Other values (60) 14779
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 4781
 
11.7%
0 3995
 
9.8%
- 3976
 
9.8%
1 3398
 
8.3%
2 2238
 
5.5%
3 1558
 
3.8%
6 1541
 
3.8%
7 1509
 
3.7%
4 1498
 
3.7%
9 1481
 
3.6%
Other values (60) 14779
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 4781
 
11.7%
0 3995
 
9.8%
- 3976
 
9.8%
1 3398
 
8.3%
2 2238
 
5.5%
3 1558
 
3.8%
6 1541
 
3.8%
7 1509
 
3.7%
4 1498
 
3.7%
9 1481
 
3.6%
Other values (60) 14779
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 4781
 
11.7%
0 3995
 
9.8%
- 3976
 
9.8%
1 3398
 
8.3%
2 2238
 
5.5%
3 1558
 
3.8%
6 1541
 
3.8%
7 1509
 
3.7%
4 1498
 
3.7%
9 1481
 
3.6%
Other values (60) 14779
36.3%

eventDate
Text

Missing 

Distinct45561
Distinct (%)12.5%
Missing239769
Missing (%)39.7%
Memory size4.6 MiB
2025-01-07T10:41:40.437290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length10
Mean length10.99102388
Min length4

Characters and Unicode

Total characters4010152
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12880 ?
Unique (%)3.5%

Sample

1st row1967-06-20
2nd row1914-07
3rd row2005-08-02
4th row1964-04-25
5th row1971-08-22
ValueCountFrequency (%)
1998-07-26 709
 
0.2%
1938 599
 
0.2%
1896 545
 
0.1%
2006-06-24 544
 
0.1%
1933 543
 
0.1%
1960-06-30 506
 
0.1%
1930 495
 
0.1%
1936 490
 
0.1%
1927-07-10 469
 
0.1%
1964-08-01/1964-08-31 449
 
0.1%
Other values (45551) 359508
98.5%
2025-01-07T10:41:40.714247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 776958
19.4%
1 696249
17.4%
0 648003
16.2%
9 488959
12.2%
2 286183
 
7.1%
6 224282
 
5.6%
7 215676
 
5.4%
8 182043
 
4.5%
5 158527
 
4.0%
3 154598
 
3.9%
Other values (2) 178674
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4010152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 776958
19.4%
1 696249
17.4%
0 648003
16.2%
9 488959
12.2%
2 286183
 
7.1%
6 224282
 
5.6%
7 215676
 
5.4%
8 182043
 
4.5%
5 158527
 
4.0%
3 154598
 
3.9%
Other values (2) 178674
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4010152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 776958
19.4%
1 696249
17.4%
0 648003
16.2%
9 488959
12.2%
2 286183
 
7.1%
6 224282
 
5.6%
7 215676
 
5.4%
8 182043
 
4.5%
5 158527
 
4.0%
3 154598
 
3.9%
Other values (2) 178674
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4010152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 776958
19.4%
1 696249
17.4%
0 648003
16.2%
9 488959
12.2%
2 286183
 
7.1%
6 224282
 
5.6%
7 215676
 
5.4%
8 182043
 
4.5%
5 158527
 
4.0%
3 154598
 
3.9%
Other values (2) 178674
 
4.5%

eventTime
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

startDayOfYear
Real number (ℝ)

Missing 

Distinct366
Distinct (%)0.1%
Missing270965
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean182.8920881
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:40.792690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41
Q1140
median187
Q3225
95-th percentile315
Maximum366
Range365
Interquartile range (IQR)85

Descriptive statistics

Standard deviation74.6471582
Coefficient of variation (CV)0.4081486465
Kurtosis0.01067070399
Mean182.8920881
Median Absolute Deviation (MAD)42
Skewness-0.1414073649
Sum61023957
Variance5572.198227
MonotonicityNot monotonic
2025-01-07T10:41:40.856197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 3298
 
0.5%
183 2901
 
0.5%
191 2876
 
0.5%
207 2734
 
0.5%
213 2713
 
0.4%
178 2623
 
0.4%
214 2602
 
0.4%
172 2574
 
0.4%
189 2556
 
0.4%
218 2541
 
0.4%
Other values (356) 306243
50.6%
(Missing) 270965
44.8%
ValueCountFrequency (%)
1 1484
0.2%
2 382
 
0.1%
3 296
 
< 0.1%
4 297
 
< 0.1%
5 218
 
< 0.1%
ValueCountFrequency (%)
366 62
 
< 0.1%
365 205
< 0.1%
364 291
< 0.1%
363 219
< 0.1%
362 223
< 0.1%

endDayOfYear
Real number (ℝ)

Missing 

Distinct366
Distinct (%)0.1%
Missing270965
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean184.8889082
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:40.917275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1142
median189
Q3227
95-th percentile319
Maximum366
Range365
Interquartile range (IQR)85

Descriptive statistics

Standard deviation74.59809418
Coefficient of variation (CV)0.4034752269
Kurtosis0.01748254344
Mean184.8889082
Median Absolute Deviation (MAD)42
Skewness-0.09622856515
Sum61690218
Variance5564.875655
MonotonicityNot monotonic
2025-01-07T10:41:40.978776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 2989
 
0.5%
191 2948
 
0.5%
197 2758
 
0.5%
212 2710
 
0.4%
182 2684
 
0.4%
178 2598
 
0.4%
181 2581
 
0.4%
196 2566
 
0.4%
172 2491
 
0.4%
208 2488
 
0.4%
Other values (356) 306848
50.8%
(Missing) 270965
44.8%
ValueCountFrequency (%)
1 365
0.1%
2 360
0.1%
3 269
< 0.1%
4 247
< 0.1%
5 230
< 0.1%
ValueCountFrequency (%)
366 350
 
0.1%
365 993
0.2%
364 289
 
< 0.1%
363 217
 
< 0.1%
362 223
 
< 0.1%

year
Real number (ℝ)

Missing 

Distinct190
Distinct (%)0.1%
Missing240229
Missing (%)39.7%
Infinite0
Infinite (%)0.0%
Mean1958.850989
Minimum1665
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:41.042843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1665
5-th percentile1907
Q11936
median1964
Q31978
95-th percentile2007
Maximum2023
Range358
Interquartile range (IQR)42

Descriptive statistics

Standard deviation30.37838909
Coefficient of variation (CV)0.01550826952
Kurtosis-0.4607607422
Mean1958.850989
Median Absolute Deviation (MAD)21
Skewness-0.2423882483
Sum713799424
Variance922.8465238
MonotonicityNot monotonic
2025-01-07T10:41:41.200179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1966 12303
 
2.0%
1968 9189
 
1.5%
1971 8968
 
1.5%
1967 8355
 
1.4%
1965 7870
 
1.3%
1972 6272
 
1.0%
1964 6145
 
1.0%
1974 6095
 
1.0%
1973 6077
 
1.0%
1963 5552
 
0.9%
Other values (180) 287571
47.6%
(Missing) 240229
39.7%
ValueCountFrequency (%)
1665 2
 
< 0.1%
1804 1
 
< 0.1%
1807 36
< 0.1%
1815 1
 
< 0.1%
1816 1
 
< 0.1%
ValueCountFrequency (%)
2023 96
< 0.1%
2022 5
 
< 0.1%
2021 9
 
< 0.1%
2020 67
 
< 0.1%
2019 182
< 0.1%

month
Real number (ℝ)

Missing 

Distinct12
Distinct (%)< 0.1%
Missing254573
Missing (%)42.1%
Infinite0
Infinite (%)0.0%
Mean6.559169611
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:41.257685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median7
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.479966525
Coefficient of variation (CV)0.3780915377
Kurtosis-0.08511274465
Mean6.559169611
Median Absolute Deviation (MAD)1
Skewness-0.1234431358
Sum2296057
Variance6.150233964
MonotonicityNot monotonic
2025-01-07T10:41:41.309856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 73156
 
12.1%
6 58086
 
9.6%
8 51402
 
8.5%
5 35620
 
5.9%
9 25573
 
4.2%
4 24539
 
4.1%
3 16420
 
2.7%
10 16139
 
2.7%
2 13949
 
2.3%
11 13286
 
2.2%
Other values (2) 21883
 
3.6%
(Missing) 254573
42.1%
ValueCountFrequency (%)
1 11770
 
1.9%
2 13949
 
2.3%
3 16420
2.7%
4 24539
4.1%
5 35620
5.9%
ValueCountFrequency (%)
12 10113
 
1.7%
11 13286
 
2.2%
10 16139
 
2.7%
9 25573
4.2%
8 51402
8.5%

day
Real number (ℝ)

Missing 

Distinct31
Distinct (%)< 0.1%
Missing314935
Missing (%)52.1%
Infinite0
Infinite (%)0.0%
Mean15.66447353
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:41.363858image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.688852146
Coefficient of variation (CV)0.5546852329
Kurtosis-1.159246063
Mean15.66447353
Median Absolute Deviation (MAD)7
Skewness0.01281841663
Sum4537857
Variance75.49615162
MonotonicityNot monotonic
2025-01-07T10:41:41.420418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
8 11096
 
1.8%
20 10742
 
1.8%
10 10614
 
1.8%
1 10586
 
1.8%
12 10579
 
1.7%
15 10517
 
1.7%
26 9863
 
1.6%
25 9824
 
1.6%
16 9809
 
1.6%
14 9721
 
1.6%
Other values (21) 186340
30.8%
(Missing) 314935
52.1%
ValueCountFrequency (%)
1 10586
1.8%
2 8916
1.5%
3 8231
1.4%
4 9093
1.5%
5 8355
1.4%
ValueCountFrequency (%)
31 5062
0.8%
30 8406
1.4%
29 8487
1.4%
28 8712
1.4%
27 8985
1.5%

verbatimEventDate
Text

Missing 

Distinct67985
Distinct (%)32.6%
Missing396306
Missing (%)65.5%
Memory size4.6 MiB
2025-01-07T10:41:41.605511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length79
Median length71
Mean length10.59670219
Min length1

Characters and Unicode

Total characters2207505
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51573 ?
Unique (%)24.8%

Sample

1st row[Not Stated]
2nd row2-Aug-2005
3rd row[Not Stated]
4th row[Not Stated]
5th row9-IX-78
ValueCountFrequency (%)
not 32197
 
8.2%
stated 32165
 
8.2%
july 8706
 
2.2%
aug 7740
 
2.0%
june 7233
 
1.8%
may 5957
 
1.5%
1968 5763
 
1.5%
1971 5705
 
1.5%
1966 4507
 
1.1%
1972 2977
 
0.8%
Other values (37313) 279737
71.2%
2025-01-07T10:41:41.902180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 217306
 
9.8%
184367
 
8.4%
9 146678
 
6.6%
- 127695
 
5.8%
2 112927
 
5.1%
t 105528
 
4.8%
I 88868
 
4.0%
6 79315
 
3.6%
0 76302
 
3.5%
. 64856
 
2.9%
Other values (82) 1003663
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2207505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 217306
 
9.8%
184367
 
8.4%
9 146678
 
6.6%
- 127695
 
5.8%
2 112927
 
5.1%
t 105528
 
4.8%
I 88868
 
4.0%
6 79315
 
3.6%
0 76302
 
3.5%
. 64856
 
2.9%
Other values (82) 1003663
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2207505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 217306
 
9.8%
184367
 
8.4%
9 146678
 
6.6%
- 127695
 
5.8%
2 112927
 
5.1%
t 105528
 
4.8%
I 88868
 
4.0%
6 79315
 
3.6%
0 76302
 
3.5%
. 64856
 
2.9%
Other values (82) 1003663
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2207505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 217306
 
9.8%
184367
 
8.4%
9 146678
 
6.6%
- 127695
 
5.8%
2 112927
 
5.1%
t 105528
 
4.8%
I 88868
 
4.0%
6 79315
 
3.6%
0 76302
 
3.5%
. 64856
 
2.9%
Other values (82) 1003663
45.5%

habitat
Text

Missing 

Distinct89
Distinct (%)44.7%
Missing604427
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:42.098246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length103
Median length43
Mean length19.28643216
Min length5

Characters and Unicode

Total characters3838
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)32.2%

Sample

1st rowRoadside in coniferous forest
2nd rowOn a figleaf gourd
3rd rowcultivated garden
4th rowhammocks-dense hardwood & Palmetto forests
5th rowvisiting mango flowers
ValueCountFrequency (%)
garden 45
 
7.4%
cultivated 44
 
7.3%
stream 26
 
4.3%
on 26
 
4.3%
forest 23
 
3.8%
in 19
 
3.1%
of 13
 
2.1%
collected 12
 
2.0%
at 9
 
1.5%
firma 8
 
1.3%
Other values (183) 381
62.9%
2025-01-07T10:41:42.346153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
407
 
10.6%
e 388
 
10.1%
a 308
 
8.0%
r 258
 
6.7%
t 250
 
6.5%
d 224
 
5.8%
n 223
 
5.8%
o 217
 
5.7%
i 190
 
5.0%
l 185
 
4.8%
Other values (52) 1188
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
407
 
10.6%
e 388
 
10.1%
a 308
 
8.0%
r 258
 
6.7%
t 250
 
6.5%
d 224
 
5.8%
n 223
 
5.8%
o 217
 
5.7%
i 190
 
5.0%
l 185
 
4.8%
Other values (52) 1188
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
407
 
10.6%
e 388
 
10.1%
a 308
 
8.0%
r 258
 
6.7%
t 250
 
6.5%
d 224
 
5.8%
n 223
 
5.8%
o 217
 
5.7%
i 190
 
5.0%
l 185
 
4.8%
Other values (52) 1188
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
407
 
10.6%
e 388
 
10.1%
a 308
 
8.0%
r 258
 
6.7%
t 250
 
6.5%
d 224
 
5.8%
n 223
 
5.8%
o 217
 
5.7%
i 190
 
5.0%
l 185
 
4.8%
Other values (52) 1188
31.0%

samplingProtocol
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

sampleSizeValue
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

sampleSizeUnit
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

samplingEffort
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

fieldNotes
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

eventRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

locationID
Unsupported

Missing  Rejected  Unsupported 

Missing603581
Missing (%)99.8%
Memory size4.6 MiB

higherGeographyID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

higherGeography
Text

Missing 

Distinct10596
Distinct (%)2.4%
Missing156072
Missing (%)25.8%
Memory size4.6 MiB
2025-01-07T10:41:42.542015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length101
Median length91
Mean length30.3893578
Min length4

Characters and Unicode

Total characters13631268
Distinct characters132
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3142 ?
Unique (%)0.7%

Sample

1st rowUnited States, [Not Stated], [Not Stated]
2nd rowCosta Rica, Cartago, [Not Stated]
3rd rowUnited States, Alaska, Aleutians West
4th rowUnited States, Virginia, Virginia Beach
5th rowUnited States, New York, [Not Stated]
ValueCountFrequency (%)
united 222825
 
12.1%
states 221093
 
12.1%
not 167986
 
9.2%
stated 167984
 
9.2%
california 23408
 
1.3%
virginia 23318
 
1.3%
new 22501
 
1.2%
colorado 21080
 
1.1%
mexico 21000
 
1.1%
canada 16228
 
0.9%
Other values (6796) 927046
50.5%
2025-01-07T10:41:42.813483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1386625
 
10.2%
t 1386616
 
10.2%
1385915
 
10.2%
e 1090858
 
8.0%
i 815973
 
6.0%
n 814117
 
6.0%
, 798806
 
5.9%
o 692454
 
5.1%
d 580356
 
4.3%
s 501626
 
3.7%
Other values (122) 4177922
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13631268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1386625
 
10.2%
t 1386616
 
10.2%
1385915
 
10.2%
e 1090858
 
8.0%
i 815973
 
6.0%
n 814117
 
6.0%
, 798806
 
5.9%
o 692454
 
5.1%
d 580356
 
4.3%
s 501626
 
3.7%
Other values (122) 4177922
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13631268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1386625
 
10.2%
t 1386616
 
10.2%
1385915
 
10.2%
e 1090858
 
8.0%
i 815973
 
6.0%
n 814117
 
6.0%
, 798806
 
5.9%
o 692454
 
5.1%
d 580356
 
4.3%
s 501626
 
3.7%
Other values (122) 4177922
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13631268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1386625
 
10.2%
t 1386616
 
10.2%
1385915
 
10.2%
e 1090858
 
8.0%
i 815973
 
6.0%
n 814117
 
6.0%
, 798806
 
5.9%
o 692454
 
5.1%
d 580356
 
4.3%
s 501626
 
3.7%
Other values (122) 4177922
30.6%

continent
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing199137
Missing (%)32.9%
Memory size4.6 MiB
2025-01-07T10:41:42.875483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length11.12657803
Min length4

Characters and Unicode

Total characters4511705
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowNORTH_AMERICA
4th rowNORTH_AMERICA
5th rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 259896
64.1%
asia 50862
 
12.5%
south_america 49534
 
12.2%
africa 21692
 
5.3%
oceania 14473
 
3.6%
europe 9029
 
2.2%
antarctica 3
 
< 0.1%
2025-01-07T10:41:42.977646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 792923
17.6%
R 600050
13.3%
I 396460
8.8%
C 345601
7.7%
E 341961
7.6%
O 332932
7.4%
T 309436
 
6.9%
H 309430
 
6.9%
_ 309430
 
6.9%
M 309430
 
6.9%
Other values (5) 464052
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4511705
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 792923
17.6%
R 600050
13.3%
I 396460
8.8%
C 345601
7.7%
E 341961
7.6%
O 332932
7.4%
T 309436
 
6.9%
H 309430
 
6.9%
_ 309430
 
6.9%
M 309430
 
6.9%
Other values (5) 464052
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4511705
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 792923
17.6%
R 600050
13.3%
I 396460
8.8%
C 345601
7.7%
E 341961
7.6%
O 332932
7.4%
T 309436
 
6.9%
H 309430
 
6.9%
_ 309430
 
6.9%
M 309430
 
6.9%
Other values (5) 464052
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4511705
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 792923
17.6%
R 600050
13.3%
I 396460
8.8%
C 345601
7.7%
E 341961
7.6%
O 332932
7.4%
T 309436
 
6.9%
H 309430
 
6.9%
_ 309430
 
6.9%
M 309430
 
6.9%
Other values (5) 464052
10.3%

waterBody
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

islandGroup
Text

Missing 

Distinct72
Distinct (%)2.9%
Missing602107
Missing (%)99.6%
Memory size4.6 MiB
2025-01-07T10:41:43.054653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length13
Mean length13.72052402
Min length5

Characters and Unicode

Total characters34562
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.8%

Sample

1st rowSunda Islands
2nd rowInner Islands
3rd rowViti Levu Group
4th rowChuuk Lagoon
5th rowSunda Islands
ValueCountFrequency (%)
islands 2159
42.2%
sunda 955
18.7%
marquesas 249
 
4.9%
solomon 226
 
4.4%
bass 171
 
3.3%
outer 149
 
2.9%
chuuk 149
 
2.9%
lagoon 149
 
2.9%
inner 140
 
2.7%
group 100
 
2.0%
Other values (78) 673
 
13.1%
2025-01-07T10:41:43.199568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 5363
15.5%
a 4393
12.7%
n 3946
11.4%
d 3264
9.4%
2601
7.5%
l 2567
7.4%
I 2312
6.7%
u 1952
 
5.6%
S 1249
 
3.6%
o 1226
 
3.5%
Other values (39) 5689
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34562
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 5363
15.5%
a 4393
12.7%
n 3946
11.4%
d 3264
9.4%
2601
7.5%
l 2567
7.4%
I 2312
6.7%
u 1952
 
5.6%
S 1249
 
3.6%
o 1226
 
3.5%
Other values (39) 5689
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34562
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 5363
15.5%
a 4393
12.7%
n 3946
11.4%
d 3264
9.4%
2601
7.5%
l 2567
7.4%
I 2312
6.7%
u 1952
 
5.6%
S 1249
 
3.6%
o 1226
 
3.5%
Other values (39) 5689
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34562
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 5363
15.5%
a 4393
12.7%
n 3946
11.4%
d 3264
9.4%
2601
7.5%
l 2567
7.4%
I 2312
6.7%
u 1952
 
5.6%
S 1249
 
3.6%
o 1226
 
3.5%
Other values (39) 5689
16.5%

island
Text

Missing 

Distinct436
Distinct (%)4.7%
Missing595261
Missing (%)98.5%
Memory size4.6 MiB
2025-01-07T10:41:43.382865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length21
Mean length9.325680726
Min length3

Characters and Unicode

Total characters87335
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)1.8%

Sample

1st rowSouth Island
2nd rowPohnpei
3rd rowSouth Island
4th rowOahu
5th rowGuadalcanal
ValueCountFrequency (%)
island 3167
21.5%
south 1636
 
11.1%
java 883
 
6.0%
levu 565
 
3.8%
viti 541
 
3.7%
north 519
 
3.5%
guadalcanal 327
 
2.2%
borneo 253
 
1.7%
hiva 247
 
1.7%
key 246
 
1.7%
Other values (438) 6371
43.2%
2025-01-07T10:41:43.632806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 12931
14.8%
n 6143
 
7.0%
l 5485
 
6.3%
o 5446
 
6.2%
5390
 
6.2%
u 4466
 
5.1%
d 4450
 
5.1%
s 4126
 
4.7%
e 3908
 
4.5%
t 3745
 
4.3%
Other values (52) 31245
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12931
14.8%
n 6143
 
7.0%
l 5485
 
6.3%
o 5446
 
6.2%
5390
 
6.2%
u 4466
 
5.1%
d 4450
 
5.1%
s 4126
 
4.7%
e 3908
 
4.5%
t 3745
 
4.3%
Other values (52) 31245
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12931
14.8%
n 6143
 
7.0%
l 5485
 
6.3%
o 5446
 
6.2%
5390
 
6.2%
u 4466
 
5.1%
d 4450
 
5.1%
s 4126
 
4.7%
e 3908
 
4.5%
t 3745
 
4.3%
Other values (52) 31245
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12931
14.8%
n 6143
 
7.0%
l 5485
 
6.3%
o 5446
 
6.2%
5390
 
6.2%
u 4466
 
5.1%
d 4450
 
5.1%
s 4126
 
4.7%
e 3908
 
4.5%
t 3745
 
4.3%
Other values (52) 31245
35.8%

countryCode
Text

Missing 

Distinct217
Distinct (%)< 0.1%
Missing163440
Missing (%)27.0%
Memory size4.6 MiB
2025-01-07T10:41:43.824689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters882372
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowCR
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 217888
49.4%
ca 16227
 
3.7%
mx 15807
 
3.6%
cn 14551
 
3.3%
br 12970
 
2.9%
cr 8902
 
2.0%
pe 7635
 
1.7%
in 7034
 
1.6%
ph 6836
 
1.5%
pa 6325
 
1.4%
Other values (207) 127011
28.8%
2025-01-07T10:41:44.045819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 226074
25.6%
S 225013
25.5%
C 60558
 
6.9%
A 35637
 
4.0%
P 33561
 
3.8%
R 32631
 
3.7%
N 30863
 
3.5%
M 28480
 
3.2%
E 27423
 
3.1%
B 22245
 
2.5%
Other values (16) 159887
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 882372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 226074
25.6%
S 225013
25.5%
C 60558
 
6.9%
A 35637
 
4.0%
P 33561
 
3.8%
R 32631
 
3.7%
N 30863
 
3.5%
M 28480
 
3.2%
E 27423
 
3.1%
B 22245
 
2.5%
Other values (16) 159887
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 882372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 226074
25.6%
S 225013
25.5%
C 60558
 
6.9%
A 35637
 
4.0%
P 33561
 
3.8%
R 32631
 
3.7%
N 30863
 
3.5%
M 28480
 
3.2%
E 27423
 
3.1%
B 22245
 
2.5%
Other values (16) 159887
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 882372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 226074
25.6%
S 225013
25.5%
C 60558
 
6.9%
A 35637
 
4.0%
P 33561
 
3.8%
R 32631
 
3.7%
N 30863
 
3.5%
M 28480
 
3.2%
E 27423
 
3.1%
B 22245
 
2.5%
Other values (16) 159887
18.1%

stateProvince
Text

Missing 

Distinct3068
Distinct (%)0.7%
Missing173217
Missing (%)28.6%
Memory size4.6 MiB
2025-01-07T10:41:44.235691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length57
Median length44
Mean length9.044864618
Min length2

Characters and Unicode

Total characters3902036
Distinct characters116
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique808 ?
Unique (%)0.2%

Sample

1st row[Not Stated]
2nd rowCartago
3rd rowAlaska
4th rowVirginia
5th rowNew York
ValueCountFrequency (%)
not 29432
 
5.2%
stated 29432
 
5.2%
california 23319
 
4.1%
virginia 22011
 
3.9%
colorado 20952
 
3.7%
new 16649
 
2.9%
texas 12340
 
2.2%
arizona 12144
 
2.1%
florida 9882
 
1.7%
maryland 9606
 
1.7%
Other values (2915) 379808
67.2%
2025-01-07T10:41:44.501331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 524269
 
13.4%
o 333137
 
8.5%
i 321738
 
8.2%
n 299056
 
7.7%
r 250043
 
6.4%
e 216664
 
5.6%
t 208608
 
5.3%
s 151897
 
3.9%
l 138272
 
3.5%
134166
 
3.4%
Other values (106) 1324186
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3902036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 524269
 
13.4%
o 333137
 
8.5%
i 321738
 
8.2%
n 299056
 
7.7%
r 250043
 
6.4%
e 216664
 
5.6%
t 208608
 
5.3%
s 151897
 
3.9%
l 138272
 
3.5%
134166
 
3.4%
Other values (106) 1324186
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3902036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 524269
 
13.4%
o 333137
 
8.5%
i 321738
 
8.2%
n 299056
 
7.7%
r 250043
 
6.4%
e 216664
 
5.6%
t 208608
 
5.3%
s 151897
 
3.9%
l 138272
 
3.5%
134166
 
3.4%
Other values (106) 1324186
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3902036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 524269
 
13.4%
o 333137
 
8.5%
i 321738
 
8.2%
n 299056
 
7.7%
r 250043
 
6.4%
e 216664
 
5.6%
t 208608
 
5.3%
s 151897
 
3.9%
l 138272
 
3.5%
134166
 
3.4%
Other values (106) 1324186
33.9%

county
Text

Missing 

Distinct4068
Distinct (%)1.2%
Missing254826
Missing (%)42.1%
Memory size4.6 MiB
2025-01-07T10:41:44.704207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length51
Median length45
Mean length9.456223556
Min length1

Characters and Unicode

Total characters3307787
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1157 ?
Unique (%)0.3%

Sample

1st row[Not Stated]
2nd row[Not Stated]
3rd rowAleutians West
4th rowVirginia Beach
5th row[Not Stated]
ValueCountFrequency (%)
not 132036
25.3%
stated 132034
25.3%
boulder 6789
 
1.3%
creek 6760
 
1.3%
clear 6751
 
1.3%
san 5404
 
1.0%
montgomery 4939
 
0.9%
cochise 4320
 
0.8%
prince 3491
 
0.7%
tuolumne 3205
 
0.6%
Other values (4079) 215253
41.3%
2025-01-07T10:41:44.981221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 455384
13.8%
a 309851
 
9.4%
e 305684
 
9.2%
o 264700
 
8.0%
171182
 
5.2%
d 169196
 
5.1%
S 152102
 
4.6%
N 137663
 
4.2%
n 133833
 
4.0%
[ 132054
 
4.0%
Other values (88) 1076138
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3307787
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 455384
13.8%
a 309851
 
9.4%
e 305684
 
9.2%
o 264700
 
8.0%
171182
 
5.2%
d 169196
 
5.1%
S 152102
 
4.6%
N 137663
 
4.2%
n 133833
 
4.0%
[ 132054
 
4.0%
Other values (88) 1076138
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3307787
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 455384
13.8%
a 309851
 
9.4%
e 305684
 
9.2%
o 264700
 
8.0%
171182
 
5.2%
d 169196
 
5.1%
S 152102
 
4.6%
N 137663
 
4.2%
n 133833
 
4.0%
[ 132054
 
4.0%
Other values (88) 1076138
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3307787
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 455384
13.8%
a 309851
 
9.4%
e 305684
 
9.2%
o 264700
 
8.0%
171182
 
5.2%
d 169196
 
5.1%
S 152102
 
4.6%
N 137663
 
4.2%
n 133833
 
4.0%
[ 132054
 
4.0%
Other values (88) 1076138
32.5%

municipality
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

locality
Text

Missing 

Distinct76610
Distinct (%)17.2%
Missing158340
Missing (%)26.2%
Memory size4.6 MiB
2025-01-07T10:41:45.197316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length400600
Median length180
Mean length23.74718454
Min length1

Characters and Unicode

Total characters10598036
Distinct characters149
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44457 ?
Unique (%)10.0%

Sample

1st row[Not Stated]
2nd rowRio Aquiares, Turrialba
3rd rowSaint Paul Island, Bering Sea
4th rowFalse Cape State Park, Wash Woods, 100 meters east of Interpreter's residence
5th row[Not Stated]
ValueCountFrequency (%)
not 65922
 
4.1%
stated 65846
 
4.1%
of 42709
 
2.7%
miles 21197
 
1.3%
kilometers 15776
 
1.0%
park 15452
 
1.0%
river 15349
 
1.0%
lake 14837
 
0.9%
near 12849
 
0.8%
creek 12664
 
0.8%
Other values (56182) 1322830
82.4%
2025-01-07T10:41:45.573294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1107495
 
10.5%
a 957781
 
9.0%
e 764045
 
7.2%
o 666061
 
6.3%
t 632687
 
6.0%
n 514072
 
4.9%
i 493725
 
4.7%
r 484811
 
4.6%
l 394187
 
3.7%
s 365079
 
3.4%
Other values (139) 4218093
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10598036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1107495
 
10.5%
a 957781
 
9.0%
e 764045
 
7.2%
o 666061
 
6.3%
t 632687
 
6.0%
n 514072
 
4.9%
i 493725
 
4.7%
r 484811
 
4.6%
l 394187
 
3.7%
s 365079
 
3.4%
Other values (139) 4218093
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10598036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1107495
 
10.5%
a 957781
 
9.0%
e 764045
 
7.2%
o 666061
 
6.3%
t 632687
 
6.0%
n 514072
 
4.9%
i 493725
 
4.7%
r 484811
 
4.6%
l 394187
 
3.7%
s 365079
 
3.4%
Other values (139) 4218093
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10598036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1107495
 
10.5%
a 957781
 
9.0%
e 764045
 
7.2%
o 666061
 
6.3%
t 632687
 
6.0%
n 514072
 
4.9%
i 493725
 
4.7%
r 484811
 
4.6%
l 394187
 
3.7%
s 365079
 
3.4%
Other values (139) 4218093
39.8%

verbatimLocality
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

verbatimElevation
Text

Missing 

Distinct1024
Distinct (%)10.3%
Missing594692
Missing (%)98.4%
Memory size4.6 MiB
2025-01-07T10:41:45.768635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length94
Median length31
Mean length8.08838333
Min length1

Characters and Unicode

Total characters80350
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique334 ?
Unique (%)3.4%

Sample

1st row140 meters
2nd row3900 feet
3rd row5940 feet
4th row180 meters
5th row3000 feet
ValueCountFrequency (%)
m 2782
 
14.5%
feet 2472
 
12.9%
meters 1521
 
7.9%
ft 1465
 
7.6%
1000 347
 
1.8%
sea 318
 
1.7%
level 318
 
1.7%
300 305
 
1.6%
near 276
 
1.4%
3200 236
 
1.2%
Other values (619) 9192
47.8%
2025-01-07T10:41:46.017340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16889
21.0%
e 9358
11.6%
9298
11.6%
t 5738
 
7.1%
m 5102
 
6.3%
f 4103
 
5.1%
1 4088
 
5.1%
5 3791
 
4.7%
2 2912
 
3.6%
. 2458
 
3.1%
Other values (44) 16613
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16889
21.0%
e 9358
11.6%
9298
11.6%
t 5738
 
7.1%
m 5102
 
6.3%
f 4103
 
5.1%
1 4088
 
5.1%
5 3791
 
4.7%
2 2912
 
3.6%
. 2458
 
3.1%
Other values (44) 16613
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16889
21.0%
e 9358
11.6%
9298
11.6%
t 5738
 
7.1%
m 5102
 
6.3%
f 4103
 
5.1%
1 4088
 
5.1%
5 3791
 
4.7%
2 2912
 
3.6%
. 2458
 
3.1%
Other values (44) 16613
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16889
21.0%
e 9358
11.6%
9298
11.6%
t 5738
 
7.1%
m 5102
 
6.3%
f 4103
 
5.1%
1 4088
 
5.1%
5 3791
 
4.7%
2 2912
 
3.6%
. 2458
 
3.1%
Other values (44) 16613
20.7%

verticalDatum
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

verbatimDepth
Text

Constant  Missing 

Distinct1
Distinct (%)16.7%
Missing604620
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:46.075339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters150
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row220m inside cave entrance
2nd row220m inside cave entrance
3rd row220m inside cave entrance
4th row220m inside cave entrance
5th row220m inside cave entrance
ValueCountFrequency (%)
220m 6
25.0%
inside 6
25.0%
cave 6
25.0%
entrance 6
25.0%
2025-01-07T10:41:46.179379image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 24
16.0%
n 18
12.0%
18
12.0%
i 12
8.0%
c 12
8.0%
2 12
8.0%
a 12
8.0%
0 6
 
4.0%
m 6
 
4.0%
d 6
 
4.0%
Other values (4) 24
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 24
16.0%
n 18
12.0%
18
12.0%
i 12
8.0%
c 12
8.0%
2 12
8.0%
a 12
8.0%
0 6
 
4.0%
m 6
 
4.0%
d 6
 
4.0%
Other values (4) 24
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 24
16.0%
n 18
12.0%
18
12.0%
i 12
8.0%
c 12
8.0%
2 12
8.0%
a 12
8.0%
0 6
 
4.0%
m 6
 
4.0%
d 6
 
4.0%
Other values (4) 24
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 24
16.0%
n 18
12.0%
18
12.0%
i 12
8.0%
c 12
8.0%
2 12
8.0%
a 12
8.0%
0 6
 
4.0%
m 6
 
4.0%
d 6
 
4.0%
Other values (4) 24
16.0%
Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:46.223083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length15.5
Mean length15.5
Min length12

Characters and Unicode

Total characters31
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowPoole, R. W.
2nd rowGarrison, Rosser W.
ValueCountFrequency (%)
w 2
33.3%
poole 1
16.7%
r 1
16.7%
garrison 1
16.7%
rosser 1
16.7%
2025-01-07T10:41:46.326412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 4
12.9%
4
12.9%
. 3
9.7%
r 3
9.7%
s 3
9.7%
, 2
 
6.5%
e 2
 
6.5%
R 2
 
6.5%
W 2
 
6.5%
P 1
 
3.2%
Other values (5) 5
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4
12.9%
4
12.9%
. 3
9.7%
r 3
9.7%
s 3
9.7%
, 2
 
6.5%
e 2
 
6.5%
R 2
 
6.5%
W 2
 
6.5%
P 1
 
3.2%
Other values (5) 5
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4
12.9%
4
12.9%
. 3
9.7%
r 3
9.7%
s 3
9.7%
, 2
 
6.5%
e 2
 
6.5%
R 2
 
6.5%
W 2
 
6.5%
P 1
 
3.2%
Other values (5) 5
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4
12.9%
4
12.9%
. 3
9.7%
r 3
9.7%
s 3
9.7%
, 2
 
6.5%
e 2
 
6.5%
R 2
 
6.5%
W 2
 
6.5%
P 1
 
3.2%
Other values (5) 5
16.1%

maximumDistanceAboveSurfaceInMeters
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

locationAccordingTo
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

locationRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

decimalLatitude
Real number (ℝ)

Missing 

Distinct38003
Distinct (%)11.9%
Missing285575
Missing (%)47.2%
Infinite0
Infinite (%)0.0%
Mean27.65266777
Minimum-65.2
Maximum75.7007
Zeros0
Zeros (%)0.0%
Negative41185
Negative (%)6.8%
Memory size4.6 MiB
2025-01-07T10:41:46.390444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-65.2
5-th percentile-25.6353
Q117.9343
median36.8548
Q340.56
95-th percentile50.9287
Maximum75.7007
Range140.9007
Interquartile range (IQR)22.6257

Descriptive statistics

Standard deviation22.42323907
Coefficient of variation (CV)0.8108888174
Kurtosis1.323523639
Mean27.65266777
Median Absolute Deviation (MAD)6.3119
Skewness-1.362971279
Sum8822611.306
Variance502.8016504
MonotonicityNot monotonic
2025-01-07T10:41:46.452474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.6891 5053
 
0.8%
60.75 3839
 
0.6%
60.7493 2462
 
0.4%
40.0925 2379
 
0.4%
38.02 2013
 
0.3%
42.7299 1697
 
0.3%
37.23 1343
 
0.2%
40.015 1287
 
0.2%
42.78 1170
 
0.2%
38.9559 1141
 
0.2%
Other values (37993) 296667
49.1%
(Missing) 285575
47.2%
ValueCountFrequency (%)
-65.2 1
 
< 0.1%
-64.8242 1
 
< 0.1%
-55.0315 4
< 0.1%
-54.9682 2
< 0.1%
-54.93 4
< 0.1%
ValueCountFrequency (%)
75.7007 3
 
< 0.1%
75.3767 2
 
< 0.1%
75 5
 
< 0.1%
71.3889 51
< 0.1%
71.37 1
 
< 0.1%

decimalLongitude
Real number (ℝ)

Missing 

Distinct36962
Distinct (%)11.6%
Missing285575
Missing (%)47.2%
Infinite0
Infinite (%)0.0%
Mean-65.69456017
Minimum-179.98
Maximum179.975
Zeros4
Zeros (%)< 0.1%
Negative270810
Negative (%)44.8%
Memory size4.6 MiB
2025-01-07T10:41:46.520353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-179.98
5-th percentile-123.054
Q1-105.644
median-82.5306
Q3-70.3525
95-th percentile105.215
Maximum179.975
Range359.955
Interquartile range (IQR)35.2915

Descriptive statistics

Standard deviation67.80686183
Coefficient of variation (CV)-1.032153372
Kurtosis3.012516082
Mean-65.69456017
Median Absolute Deviation (MAD)21.4606
Skewness1.890283843
Sum-20959915.12
Variance4597.770511
MonotonicityNot monotonic
2025-01-07T10:41:46.587675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-105.644 5103
 
0.8%
-139.5 3837
 
0.6%
-139.504 2462
 
0.4%
-105.358 2379
 
0.4%
-87.8123 1697
 
0.3%
-119.93 1404
 
0.2%
-105.27 1361
 
0.2%
-80.4178 1322
 
0.2%
-0.365 1301
 
0.2%
-87.76 1163
 
0.2%
Other values (36952) 297022
49.1%
(Missing) 285575
47.2%
ValueCountFrequency (%)
-179.98 1
 
< 0.1%
-179.973 12
< 0.1%
-179.956 2
 
< 0.1%
-179.954 5
< 0.1%
-179 1
 
< 0.1%
ValueCountFrequency (%)
179.975 2
 
< 0.1%
179.9 7
< 0.1%
179.766 1
 
< 0.1%
179.467 1
 
< 0.1%
179.444 1
 
< 0.1%

coordinateUncertaintyInMeters
Real number (ℝ)

Missing 

Distinct1493
Distinct (%)12.5%
Missing592674
Missing (%)98.0%
Infinite0
Infinite (%)0.0%
Mean11207.04058
Minimum10
Maximum855042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:46.650769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile301
Q12159
median3371
Q311013
95-th percentile40318
Maximum855042
Range855032
Interquartile range (IQR)8854

Descriptive statistics

Standard deviation27466.25097
Coefficient of variation (CV)2.450803205
Kurtosis296.9992074
Mean11207.04058
Median Absolute Deviation (MAD)2600
Skewness13.18202611
Sum133946549
Variance754394942.4
MonotonicityNot monotonic
2025-01-07T10:41:46.714758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3036 1744
 
0.3%
301 466
 
0.1%
34239 426
 
0.1%
1189 258
 
< 0.1%
20000 247
 
< 0.1%
3048 220
 
< 0.1%
15000 199
 
< 0.1%
52150 194
 
< 0.1%
14563 162
 
< 0.1%
9346 135
 
< 0.1%
Other values (1483) 7901
 
1.3%
(Missing) 592674
98.0%
ValueCountFrequency (%)
10 1
 
< 0.1%
18 7
< 0.1%
25 1
 
< 0.1%
31 1
 
< 0.1%
40 1
 
< 0.1%
ValueCountFrequency (%)
855042 3
 
< 0.1%
621466 1
 
< 0.1%
496851 1
 
< 0.1%
419855 7
< 0.1%
304151 10
< 0.1%

coordinatePrecision
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

pointRadiusSpatialFit
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1680860.5
Minimum1424710
Maximum1937011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:46.766757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1424710
5-th percentile1450325.05
Q11552785.25
median1680860.5
Q31808935.75
95-th percentile1911395.95
Maximum1937011
Range512301
Interquartile range (IQR)256150.5

Descriptive statistics

Standard deviation362251.5111
Coefficient of variation (CV)0.2155155119
Kurtosisnan
Mean1680860.5
Median Absolute Deviation (MAD)256150.5
Skewnessnan
Sum3361721
Variance1.312261573 × 1011
MonotonicityStrictly decreasing
2025-01-07T10:41:46.814642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1937011 1
 
< 0.1%
1424710 1
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
1424710 1
< 0.1%
1937011 1
< 0.1%
ValueCountFrequency (%)
1937011 1
< 0.1%
1424710 1
< 0.1%

verbatimCoordinateSystem
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:46.849641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters23
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowDegrees Minutes Seconds
ValueCountFrequency (%)
degrees 1
33.3%
minutes 1
33.3%
seconds 1
33.3%
2025-01-07T10:41:46.944932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5
21.7%
s 3
13.0%
2
 
8.7%
n 2
 
8.7%
g 1
 
4.3%
r 1
 
4.3%
D 1
 
4.3%
M 1
 
4.3%
i 1
 
4.3%
u 1
 
4.3%
Other values (5) 5
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5
21.7%
s 3
13.0%
2
 
8.7%
n 2
 
8.7%
g 1
 
4.3%
r 1
 
4.3%
D 1
 
4.3%
M 1
 
4.3%
i 1
 
4.3%
u 1
 
4.3%
Other values (5) 5
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5
21.7%
s 3
13.0%
2
 
8.7%
n 2
 
8.7%
g 1
 
4.3%
r 1
 
4.3%
D 1
 
4.3%
M 1
 
4.3%
i 1
 
4.3%
u 1
 
4.3%
Other values (5) 5
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5
21.7%
s 3
13.0%
2
 
8.7%
n 2
 
8.7%
g 1
 
4.3%
r 1
 
4.3%
D 1
 
4.3%
M 1
 
4.3%
i 1
 
4.3%
u 1
 
4.3%
Other values (5) 5
21.7%

verbatimSRS
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:46.991419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1973-05-08
ValueCountFrequency (%)
1973-05-08 1
100.0%
2025-01-07T10:41:47.089084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2
20.0%
- 2
20.0%
1 1
10.0%
9 1
10.0%
3 1
10.0%
7 1
10.0%
5 1
10.0%
8 1
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
20.0%
- 2
20.0%
1 1
10.0%
9 1
10.0%
3 1
10.0%
7 1
10.0%
5 1
10.0%
8 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
20.0%
- 2
20.0%
1 1
10.0%
9 1
10.0%
3 1
10.0%
7 1
10.0%
5 1
10.0%
8 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
20.0%
- 2
20.0%
1 1
10.0%
9 1
10.0%
3 1
10.0%
7 1
10.0%
5 1
10.0%
8 1
10.0%

footprintWKT
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

footprintSRS
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean128
Minimum128
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:47.145434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum128
5-th percentile128
Q1128
median128
Q3128
95-th percentile128
Maximum128
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean128
Median Absolute Deviation (MAD)0
Skewnessnan
Sum128
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:41:47.189734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
128 1
 
< 0.1%
(Missing) 604625
> 99.9%
ValueCountFrequency (%)
128 1
< 0.1%
ValueCountFrequency (%)
128 1
< 0.1%

footprintSpatialFit
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean128
Minimum128
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:47.237611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum128
5-th percentile128
Q1128
median128
Q3128
95-th percentile128
Maximum128
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean128
Median Absolute Deviation (MAD)0
Skewnessnan
Sum128
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:41:47.285744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
128 1
 
< 0.1%
(Missing) 604625
> 99.9%
ValueCountFrequency (%)
128 1
< 0.1%
ValueCountFrequency (%)
128 1
< 0.1%

georeferencedBy
Unsupported

Missing  Rejected  Unsupported 

Missing604623
Missing (%)> 99.9%
Memory size4.6 MiB

georeferencedDate
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean5
Minimum5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:47.330184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15
median5
Q35
95-th percentile5
Maximum5
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean5
Median Absolute Deviation (MAD)0
Skewnessnan
Sum5
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:41:47.375183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
5 1
 
< 0.1%
(Missing) 604625
> 99.9%
ValueCountFrequency (%)
5 1
< 0.1%
ValueCountFrequency (%)
5 1
< 0.1%

georeferenceProtocol
Text

Missing 

Distinct65
Distinct (%)< 0.1%
Missing366755
Missing (%)60.7%
Memory size4.6 MiB
2025-01-07T10:41:47.440029image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length72
Median length12
Mean length10.94743369
Min length1

Characters and Unicode

Total characters2604077
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowGoogle Maps
2nd rowGoogle Earth
3rd rowGoogle Earth
4th rowGEOLocate
5th rowGoogle Earth
ValueCountFrequency (%)
google 163378
40.4%
earth 120763
29.8%
geolocate 70753
17.5%
maps 42641
 
10.5%
gps 1516
 
0.4%
coordinates 782
 
0.2%
centroid 781
 
0.2%
geonames 718
 
0.2%
from 711
 
0.2%
country 671
 
0.2%
Other values (106) 2062
 
0.5%
2025-01-07T10:41:47.588582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 402567
15.5%
e 238609
9.2%
a 237477
9.1%
G 236541
9.1%
t 194803
7.5%
E 191420
7.4%
l 169480
 
6.5%
166905
 
6.4%
g 163810
 
6.3%
r 124366
 
4.8%
Other values (51) 478099
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2604077
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 402567
15.5%
e 238609
9.2%
a 237477
9.1%
G 236541
9.1%
t 194803
7.5%
E 191420
7.4%
l 169480
 
6.5%
166905
 
6.4%
g 163810
 
6.3%
r 124366
 
4.8%
Other values (51) 478099
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2604077
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 402567
15.5%
e 238609
9.2%
a 237477
9.1%
G 236541
9.1%
t 194803
7.5%
E 191420
7.4%
l 169480
 
6.5%
166905
 
6.4%
g 163810
 
6.3%
r 124366
 
4.8%
Other values (51) 478099
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2604077
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 402567
15.5%
e 238609
9.2%
a 237477
9.1%
G 236541
9.1%
t 194803
7.5%
E 191420
7.4%
l 169480
 
6.5%
166905
 
6.4%
g 163810
 
6.3%
r 124366
 
4.8%
Other values (51) 478099
18.4%

georeferenceSources
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:47.633951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length7.5
Min length7

Characters and Unicode

Total characters15
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row9 March
2nd row8.v.1973
ValueCountFrequency (%)
9 1
33.3%
march 1
33.3%
8.v.1973 1
33.3%
2025-01-07T10:41:47.730881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 2
13.3%
. 2
13.3%
1
 
6.7%
a 1
 
6.7%
M 1
 
6.7%
r 1
 
6.7%
c 1
 
6.7%
h 1
 
6.7%
8 1
 
6.7%
v 1
 
6.7%
Other values (3) 3
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 2
13.3%
. 2
13.3%
1
 
6.7%
a 1
 
6.7%
M 1
 
6.7%
r 1
 
6.7%
c 1
 
6.7%
h 1
 
6.7%
8 1
 
6.7%
v 1
 
6.7%
Other values (3) 3
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 2
13.3%
. 2
13.3%
1
 
6.7%
a 1
 
6.7%
M 1
 
6.7%
r 1
 
6.7%
c 1
 
6.7%
h 1
 
6.7%
8 1
 
6.7%
v 1
 
6.7%
Other values (3) 3
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 2
13.3%
. 2
13.3%
1
 
6.7%
a 1
 
6.7%
M 1
 
6.7%
r 1
 
6.7%
c 1
 
6.7%
h 1
 
6.7%
8 1
 
6.7%
v 1
 
6.7%
Other values (3) 3
20.0%

georeferenceRemarks
Text

Missing 

Distinct1134
Distinct (%)13.4%
Missing596178
Missing (%)98.6%
Memory size4.6 MiB
2025-01-07T10:41:47.913040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length200
Median length182
Mean length45.17341383
Min length10

Characters and Unicode

Total characters381625
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique400 ?
Unique (%)4.7%

Sample

1st rowCoordinate Uncertainty In Meters: 56182
2nd rowCoordinate Uncertainty In Meters: 49611
3rd rowCoordinate Uncertainty In Meters: 97700
4th rowCoordinate Uncertainty In Meters: 41787
5th rowCoordinate Uncertainty In Meters: 71236
ValueCountFrequency (%)
in 8278
17.4%
coordinate 8139
17.1%
meters 8139
17.1%
uncertainty 8139
17.1%
coordinate-degrees 1307
 
2.7%
verbatim 1307
 
2.7%
minutes 1307
 
2.7%
3792 274
 
0.6%
the 221
 
0.5%
6066 174
 
0.4%
Other values (1273) 10423
21.8%
2025-01-07T10:41:48.170860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 42267
 
11.1%
39260
 
10.3%
t 37512
 
9.8%
n 36163
 
9.5%
r 29378
 
7.7%
i 21344
 
5.6%
o 20135
 
5.3%
a 19989
 
5.2%
s 11758
 
3.1%
d 9749
 
2.6%
Other values (59) 114070
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 381625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 42267
 
11.1%
39260
 
10.3%
t 37512
 
9.8%
n 36163
 
9.5%
r 29378
 
7.7%
i 21344
 
5.6%
o 20135
 
5.3%
a 19989
 
5.2%
s 11758
 
3.1%
d 9749
 
2.6%
Other values (59) 114070
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 381625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 42267
 
11.1%
39260
 
10.3%
t 37512
 
9.8%
n 36163
 
9.5%
r 29378
 
7.7%
i 21344
 
5.6%
o 20135
 
5.3%
a 19989
 
5.2%
s 11758
 
3.1%
d 9749
 
2.6%
Other values (59) 114070
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 381625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 42267
 
11.1%
39260
 
10.3%
t 37512
 
9.8%
n 36163
 
9.5%
r 29378
 
7.7%
i 21344
 
5.6%
o 20135
 
5.3%
a 19989
 
5.2%
s 11758
 
3.1%
d 9749
 
2.6%
Other values (59) 114070
29.9%

geologicalContextID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

earliestEonOrLowestEonothem
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB
Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:48.323066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length70
Median length65.5
Mean length65.5
Min length61

Characters and Unicode

Total characters131
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowAnimalia, Arthropoda, Insecta, Lepidoptera, Papilionidae, Papilioninae
2nd rowAnimalia, Arthropoda, Insecta, Odonata, Anisoptera, Aeshnidae
ValueCountFrequency (%)
animalia 2
16.7%
arthropoda 2
16.7%
insecta 2
16.7%
lepidoptera 1
8.3%
papilionidae 1
8.3%
papilioninae 1
8.3%
odonata 1
8.3%
anisoptera 1
8.3%
aeshnidae 1
8.3%
2025-01-07T10:41:48.440473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 17
13.0%
i 13
 
9.9%
n 10
 
7.6%
10
 
7.6%
, 10
 
7.6%
o 9
 
6.9%
e 9
 
6.9%
p 7
 
5.3%
t 7
 
5.3%
d 6
 
4.6%
Other values (11) 33
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 17
13.0%
i 13
 
9.9%
n 10
 
7.6%
10
 
7.6%
, 10
 
7.6%
o 9
 
6.9%
e 9
 
6.9%
p 7
 
5.3%
t 7
 
5.3%
d 6
 
4.6%
Other values (11) 33
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 17
13.0%
i 13
 
9.9%
n 10
 
7.6%
10
 
7.6%
, 10
 
7.6%
o 9
 
6.9%
e 9
 
6.9%
p 7
 
5.3%
t 7
 
5.3%
d 6
 
4.6%
Other values (11) 33
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 17
13.0%
i 13
 
9.9%
n 10
 
7.6%
10
 
7.6%
, 10
 
7.6%
o 9
 
6.9%
e 9
 
6.9%
p 7
 
5.3%
t 7
 
5.3%
d 6
 
4.6%
Other values (11) 33
25.2%

earliestEraOrLowestErathem
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:48.486221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimalia
2nd rowAnimalia
ValueCountFrequency (%)
animalia 2
100.0%
2025-01-07T10:41:48.576737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4
25.0%
i 4
25.0%
n 2
12.5%
A 2
12.5%
m 2
12.5%
l 2
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4
25.0%
i 4
25.0%
n 2
12.5%
A 2
12.5%
m 2
12.5%
l 2
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4
25.0%
i 4
25.0%
n 2
12.5%
A 2
12.5%
m 2
12.5%
l 2
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4
25.0%
i 4
25.0%
n 2
12.5%
A 2
12.5%
m 2
12.5%
l 2
12.5%

latestEraOrHighestErathem
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:48.620592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters20
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArthropoda
2nd rowArthropoda
ValueCountFrequency (%)
arthropoda 2
100.0%
2025-01-07T10:41:48.714815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 4
20.0%
o 4
20.0%
t 2
10.0%
A 2
10.0%
h 2
10.0%
p 2
10.0%
d 2
10.0%
a 2
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 4
20.0%
o 4
20.0%
t 2
10.0%
A 2
10.0%
h 2
10.0%
p 2
10.0%
d 2
10.0%
a 2
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 4
20.0%
o 4
20.0%
t 2
10.0%
A 2
10.0%
h 2
10.0%
p 2
10.0%
d 2
10.0%
a 2
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 4
20.0%
o 4
20.0%
t 2
10.0%
A 2
10.0%
h 2
10.0%
p 2
10.0%
d 2
10.0%
a 2
10.0%

earliestPeriodOrLowestSystem
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:48.755805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters14
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInsecta
2nd rowInsecta
ValueCountFrequency (%)
insecta 2
100.0%
2025-01-07T10:41:48.846632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 2
14.3%
n 2
14.3%
s 2
14.3%
e 2
14.3%
c 2
14.3%
t 2
14.3%
a 2
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 2
14.3%
n 2
14.3%
s 2
14.3%
e 2
14.3%
c 2
14.3%
t 2
14.3%
a 2
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 2
14.3%
n 2
14.3%
s 2
14.3%
e 2
14.3%
c 2
14.3%
t 2
14.3%
a 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 2
14.3%
n 2
14.3%
s 2
14.3%
e 2
14.3%
c 2
14.3%
t 2
14.3%
a 2
14.3%
Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:48.891106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length9
Mean length9
Min length7

Characters and Unicode

Total characters18
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowLepidoptera
2nd rowOdonata
ValueCountFrequency (%)
lepidoptera 1
50.0%
odonata 1
50.0%
2025-01-07T10:41:48.997873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3
16.7%
p 2
11.1%
e 2
11.1%
t 2
11.1%
d 2
11.1%
o 2
11.1%
L 1
 
5.6%
i 1
 
5.6%
r 1
 
5.6%
O 1
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3
16.7%
p 2
11.1%
e 2
11.1%
t 2
11.1%
d 2
11.1%
o 2
11.1%
L 1
 
5.6%
i 1
 
5.6%
r 1
 
5.6%
O 1
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3
16.7%
p 2
11.1%
e 2
11.1%
t 2
11.1%
d 2
11.1%
o 2
11.1%
L 1
 
5.6%
i 1
 
5.6%
r 1
 
5.6%
O 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3
16.7%
p 2
11.1%
e 2
11.1%
t 2
11.1%
d 2
11.1%
o 2
11.1%
L 1
 
5.6%
i 1
 
5.6%
r 1
 
5.6%
O 1
 
5.6%

earliestEpochOrLowestSeries
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB
Distinct4
Distinct (%)100.0%
Missing604622
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:49.052381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length10.5
Mean length14.25
Min length4

Characters and Unicode

Total characters57
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowPapilionidae
2nd rowUnited States, Florida, Pinellas
3rd rowAeshnidae
4th rowPeru
ValueCountFrequency (%)
papilionidae 1
14.3%
united 1
14.3%
states 1
14.3%
florida 1
14.3%
pinellas 1
14.3%
aeshnidae 1
14.3%
peru 1
14.3%
2025-01-07T10:41:49.157294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 7
12.3%
e 7
12.3%
a 6
10.5%
l 4
 
7.0%
d 4
 
7.0%
n 4
 
7.0%
3
 
5.3%
P 3
 
5.3%
s 3
 
5.3%
t 3
 
5.3%
Other values (10) 13
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 7
12.3%
e 7
12.3%
a 6
10.5%
l 4
 
7.0%
d 4
 
7.0%
n 4
 
7.0%
3
 
5.3%
P 3
 
5.3%
s 3
 
5.3%
t 3
 
5.3%
Other values (10) 13
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 7
12.3%
e 7
12.3%
a 6
10.5%
l 4
 
7.0%
d 4
 
7.0%
n 4
 
7.0%
3
 
5.3%
P 3
 
5.3%
s 3
 
5.3%
t 3
 
5.3%
Other values (10) 13
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 7
12.3%
e 7
12.3%
a 6
10.5%
l 4
 
7.0%
d 4
 
7.0%
n 4
 
7.0%
3
 
5.3%
P 3
 
5.3%
s 3
 
5.3%
t 3
 
5.3%
Other values (10) 13
22.8%
Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:49.205068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters26
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowNORTH_AMERICA
2nd rowSOUTH_AMERICA
ValueCountFrequency (%)
north_america 1
50.0%
south_america 1
50.0%
2025-01-07T10:41:49.299794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4
15.4%
R 3
11.5%
T 2
7.7%
H 2
7.7%
O 2
7.7%
I 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
C 2
7.7%
Other values (3) 3
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4
15.4%
R 3
11.5%
T 2
7.7%
H 2
7.7%
O 2
7.7%
I 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
C 2
7.7%
Other values (3) 3
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4
15.4%
R 3
11.5%
T 2
7.7%
H 2
7.7%
O 2
7.7%
I 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
C 2
7.7%
Other values (3) 3
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4
15.4%
R 3
11.5%
T 2
7.7%
H 2
7.7%
O 2
7.7%
I 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
C 2
7.7%
Other values (3) 3
11.5%

latestAgeOrHighestStage
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

lowestBiostratigraphicZone
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB
Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:49.343305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length8.5
Mean length8.5
Min length7

Characters and Unicode

Total characters17
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowTroides
2nd rowGynacantha
ValueCountFrequency (%)
troides 1
50.0%
gynacantha 1
50.0%
2025-01-07T10:41:49.447357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3
17.6%
n 2
11.8%
o 1
 
5.9%
i 1
 
5.9%
T 1
 
5.9%
r 1
 
5.9%
e 1
 
5.9%
d 1
 
5.9%
G 1
 
5.9%
s 1
 
5.9%
Other values (4) 4
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3
17.6%
n 2
11.8%
o 1
 
5.9%
i 1
 
5.9%
T 1
 
5.9%
r 1
 
5.9%
e 1
 
5.9%
d 1
 
5.9%
G 1
 
5.9%
s 1
 
5.9%
Other values (4) 4
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3
17.6%
n 2
11.8%
o 1
 
5.9%
i 1
 
5.9%
T 1
 
5.9%
r 1
 
5.9%
e 1
 
5.9%
d 1
 
5.9%
G 1
 
5.9%
s 1
 
5.9%
Other values (4) 4
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3
17.6%
n 2
11.8%
o 1
 
5.9%
i 1
 
5.9%
T 1
 
5.9%
r 1
 
5.9%
e 1
 
5.9%
d 1
 
5.9%
G 1
 
5.9%
s 1
 
5.9%
Other values (4) 4
23.5%
Distinct4
Distinct (%)100.0%
Missing604622
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:49.499038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length8.5
Mean length5.25
Min length2

Characters and Unicode

Total characters21
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowTroides
2nd rowUS
3rd rowGynacantha
4th rowPE
ValueCountFrequency (%)
troides 1
25.0%
us 1
25.0%
gynacantha 1
25.0%
pe 1
25.0%
2025-01-07T10:41:49.611298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3
 
14.3%
n 2
 
9.5%
r 1
 
4.8%
T 1
 
4.8%
o 1
 
4.8%
i 1
 
4.8%
s 1
 
4.8%
U 1
 
4.8%
d 1
 
4.8%
e 1
 
4.8%
Other values (8) 8
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3
 
14.3%
n 2
 
9.5%
r 1
 
4.8%
T 1
 
4.8%
o 1
 
4.8%
i 1
 
4.8%
s 1
 
4.8%
U 1
 
4.8%
d 1
 
4.8%
e 1
 
4.8%
Other values (8) 8
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3
 
14.3%
n 2
 
9.5%
r 1
 
4.8%
T 1
 
4.8%
o 1
 
4.8%
i 1
 
4.8%
s 1
 
4.8%
U 1
 
4.8%
d 1
 
4.8%
e 1
 
4.8%
Other values (8) 8
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3
 
14.3%
n 2
 
9.5%
r 1
 
4.8%
T 1
 
4.8%
o 1
 
4.8%
i 1
 
4.8%
s 1
 
4.8%
U 1
 
4.8%
d 1
 
4.8%
e 1
 
4.8%
Other values (8) 8
38.1%

group
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:49.658813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowFlorida
ValueCountFrequency (%)
florida 1
100.0%
2025-01-07T10:41:49.753601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 1
14.3%
l 1
14.3%
o 1
14.3%
r 1
14.3%
i 1
14.3%
d 1
14.3%
a 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 1
14.3%
l 1
14.3%
o 1
14.3%
r 1
14.3%
i 1
14.3%
d 1
14.3%
a 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 1
14.3%
l 1
14.3%
o 1
14.3%
r 1
14.3%
i 1
14.3%
d 1
14.3%
a 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 1
14.3%
l 1
14.3%
o 1
14.3%
r 1
14.3%
i 1
14.3%
d 1
14.3%
a 1
14.3%

formation
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:49.795007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPinellas
ValueCountFrequency (%)
pinellas 1
100.0%
2025-01-07T10:41:49.885179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 2
25.0%
i 1
12.5%
P 1
12.5%
n 1
12.5%
e 1
12.5%
a 1
12.5%
s 1
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2
25.0%
i 1
12.5%
P 1
12.5%
n 1
12.5%
e 1
12.5%
a 1
12.5%
s 1
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2
25.0%
i 1
12.5%
P 1
12.5%
n 1
12.5%
e 1
12.5%
a 1
12.5%
s 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2
25.0%
i 1
12.5%
P 1
12.5%
n 1
12.5%
e 1
12.5%
a 1
12.5%
s 1
12.5%

member
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:49.931775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10
Min length9

Characters and Unicode

Total characters20
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowamphrysus
2nd rowmembranalis
ValueCountFrequency (%)
amphrysus 1
50.0%
membranalis 1
50.0%
2025-01-07T10:41:50.044266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3
15.0%
m 3
15.0%
s 3
15.0%
r 2
10.0%
p 1
 
5.0%
h 1
 
5.0%
y 1
 
5.0%
u 1
 
5.0%
e 1
 
5.0%
b 1
 
5.0%
Other values (3) 3
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3
15.0%
m 3
15.0%
s 3
15.0%
r 2
10.0%
p 1
 
5.0%
h 1
 
5.0%
y 1
 
5.0%
u 1
 
5.0%
e 1
 
5.0%
b 1
 
5.0%
Other values (3) 3
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3
15.0%
m 3
15.0%
s 3
15.0%
r 2
10.0%
p 1
 
5.0%
h 1
 
5.0%
y 1
 
5.0%
u 1
 
5.0%
e 1
 
5.0%
b 1
 
5.0%
Other values (3) 3
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3
15.0%
m 3
15.0%
s 3
15.0%
r 2
10.0%
p 1
 
5.0%
h 1
 
5.0%
y 1
 
5.0%
u 1
 
5.0%
e 1
 
5.0%
b 1
 
5.0%
Other values (3) 3
15.0%

bed
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:50.100398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length22.5
Mean length22.5
Min length14

Characters and Unicode

Total characters45
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowSt. Petersburg
2nd rowHuaru Valley, 90 mi. N. of Lima
ValueCountFrequency (%)
st 1
11.1%
petersburg 1
11.1%
huaru 1
11.1%
valley 1
11.1%
90 1
11.1%
mi 1
11.1%
n 1
11.1%
of 1
11.1%
lima 1
11.1%
2025-01-07T10:41:50.215038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7
15.6%
r 3
 
6.7%
. 3
 
6.7%
e 3
 
6.7%
u 3
 
6.7%
a 3
 
6.7%
l 2
 
4.4%
i 2
 
4.4%
t 2
 
4.4%
m 2
 
4.4%
Other values (15) 15
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7
15.6%
r 3
 
6.7%
. 3
 
6.7%
e 3
 
6.7%
u 3
 
6.7%
a 3
 
6.7%
l 2
 
4.4%
i 2
 
4.4%
t 2
 
4.4%
m 2
 
4.4%
Other values (15) 15
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7
15.6%
r 3
 
6.7%
. 3
 
6.7%
e 3
 
6.7%
u 3
 
6.7%
a 3
 
6.7%
l 2
 
4.4%
i 2
 
4.4%
t 2
 
4.4%
m 2
 
4.4%
Other values (15) 15
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7
15.6%
r 3
 
6.7%
. 3
 
6.7%
e 3
 
6.7%
u 3
 
6.7%
a 3
 
6.7%
l 2
 
4.4%
i 2
 
4.4%
t 2
 
4.4%
m 2
 
4.4%
Other values (15) 15
33.3%

identificationID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

verbatimIdentification
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:50.260038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters14
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSPECIES
2nd rowSPECIES
ValueCountFrequency (%)
species 2
100.0%
2025-01-07T10:41:50.353857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 4
28.6%
E 4
28.6%
P 2
14.3%
C 2
14.3%
I 2
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4
28.6%
E 4
28.6%
P 2
14.3%
C 2
14.3%
I 2
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4
28.6%
E 4
28.6%
P 2
14.3%
C 2
14.3%
I 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4
28.6%
E 4
28.6%
P 2
14.3%
C 2
14.3%
I 2
14.3%
Distinct15
Distinct (%)1.0%
Missing603189
Missing (%)99.8%
Memory size4.6 MiB
2025-01-07T10:41:50.408674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length9
Mean length5.812108559
Min length2

Characters and Unicode

Total characters8352
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rownear
2nd rowuncertain
3rd rownear
4th rownear
5th rowcf.
ValueCountFrequency (%)
near 466
31.6%
uncertain 459
31.2%
cf 238
16.2%
group 113
 
7.7%
subgroup 80
 
5.4%
complex 26
 
1.8%
aff 21
 
1.4%
sp 21
 
1.4%
n 15
 
1.0%
sensu 11
 
0.7%
Other values (5) 23
 
1.6%
2025-01-07T10:41:50.520219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1418
17.0%
r 1131
13.5%
e 962
11.5%
a 947
11.3%
u 743
8.9%
c 732
8.8%
t 481
 
5.8%
i 470
 
5.6%
f 280
 
3.4%
p 240
 
2.9%
Other values (12) 948
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1418
17.0%
r 1131
13.5%
e 962
11.5%
a 947
11.3%
u 743
8.9%
c 732
8.8%
t 481
 
5.8%
i 470
 
5.6%
f 280
 
3.4%
p 240
 
2.9%
Other values (12) 948
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1418
17.0%
r 1131
13.5%
e 962
11.5%
a 947
11.3%
u 743
8.9%
c 732
8.8%
t 481
 
5.8%
i 470
 
5.6%
f 280
 
3.4%
p 240
 
2.9%
Other values (12) 948
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1418
17.0%
r 1131
13.5%
e 962
11.5%
a 947
11.3%
u 743
8.9%
c 732
8.8%
t 481
 
5.8%
i 470
 
5.6%
f 280
 
3.4%
p 240
 
2.9%
Other values (12) 948
11.4%

typeStatus
Text

Missing 

Distinct11
Distinct (%)< 0.1%
Missing486591
Missing (%)80.5%
Memory size4.6 MiB
2025-01-07T10:41:50.572219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length8
Mean length6.818274241
Min length4

Characters and Unicode

Total characters804795
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPARATYPE
2nd rowTYPE
3rd rowHOLOTYPE
4th rowTYPE
5th rowSYNTYPE
ValueCountFrequency (%)
holotype 53956
45.7%
type 32775
27.8%
syntype 13266
 
11.2%
paratype 11028
 
9.3%
lectotype 5190
 
4.4%
allotype 1078
 
0.9%
neotype 315
 
0.3%
cotype 303
 
0.3%
paralectotype 120
 
0.1%
paraneotype 3
 
< 0.1%
2025-01-07T10:41:50.680335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 131301
16.3%
P 129186
16.1%
E 123664
15.4%
T 123345
15.3%
O 114923
14.3%
L 61424
7.6%
H 53956
6.7%
A 23381
 
2.9%
N 13585
 
1.7%
S 13266
 
1.6%
Other values (2) 16764
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 804795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 131301
16.3%
P 129186
16.1%
E 123664
15.4%
T 123345
15.3%
O 114923
14.3%
L 61424
7.6%
H 53956
6.7%
A 23381
 
2.9%
N 13585
 
1.7%
S 13266
 
1.6%
Other values (2) 16764
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 804795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 131301
16.3%
P 129186
16.1%
E 123664
15.4%
T 123345
15.3%
O 114923
14.3%
L 61424
7.6%
H 53956
6.7%
A 23381
 
2.9%
N 13585
 
1.7%
S 13266
 
1.6%
Other values (2) 16764
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 804795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 131301
16.3%
P 129186
16.1%
E 123664
15.4%
T 123345
15.3%
O 114923
14.3%
L 61424
7.6%
H 53956
6.7%
A 23381
 
2.9%
N 13585
 
1.7%
S 13266
 
1.6%
Other values (2) 16764
 
2.1%

identifiedBy
Text

Missing 

Distinct2736
Distinct (%)1.8%
Missing454955
Missing (%)75.2%
Memory size4.6 MiB
2025-01-07T10:41:50.879283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length150
Median length106
Mean length27.79390129
Min length2

Characters and Unicode

Total characters4159941
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique933 ?
Unique (%)0.6%

Sample

1st rowWestfall, M. J., Jr.
2nd rowDonnelly, Thomas W.
3rd rowFlint, Oliver S., Jr., (ENT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
4th rowKormann, K.
5th rowDeMarmels
ValueCountFrequency (%)
w 28127
 
4.4%
united 24410
 
3.8%
states 24409
 
3.8%
22736
 
3.5%
of 21999
 
3.4%
s 21914
 
3.4%
institution 21909
 
3.4%
smithsonian 21909
 
3.4%
museum 21366
 
3.3%
natural 21088
 
3.3%
Other values (2399) 413039
64.2%
2025-01-07T10:41:51.163017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
493235
 
11.9%
i 250985
 
6.0%
o 231935
 
5.6%
t 230915
 
5.6%
n 230479
 
5.5%
a 200365
 
4.8%
, 193541
 
4.7%
r 182836
 
4.4%
. 170349
 
4.1%
s 166922
 
4.0%
Other values (61) 1808379
43.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4159941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
493235
 
11.9%
i 250985
 
6.0%
o 231935
 
5.6%
t 230915
 
5.6%
n 230479
 
5.5%
a 200365
 
4.8%
, 193541
 
4.7%
r 182836
 
4.4%
. 170349
 
4.1%
s 166922
 
4.0%
Other values (61) 1808379
43.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4159941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
493235
 
11.9%
i 250985
 
6.0%
o 231935
 
5.6%
t 230915
 
5.6%
n 230479
 
5.5%
a 200365
 
4.8%
, 193541
 
4.7%
r 182836
 
4.4%
. 170349
 
4.1%
s 166922
 
4.0%
Other values (61) 1808379
43.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4159941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
493235
 
11.9%
i 250985
 
6.0%
o 231935
 
5.6%
t 230915
 
5.6%
n 230479
 
5.5%
a 200365
 
4.8%
, 193541
 
4.7%
r 182836
 
4.4%
. 170349
 
4.1%
s 166922
 
4.0%
Other values (61) 1808379
43.5%

identifiedByID
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:51.214918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACCEPTED
2nd rowACCEPTED
ValueCountFrequency (%)
accepted 2
100.0%
2025-01-07T10:41:51.416018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 4
25.0%
E 4
25.0%
A 2
12.5%
P 2
12.5%
T 2
12.5%
D 2
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 4
25.0%
E 4
25.0%
A 2
12.5%
P 2
12.5%
T 2
12.5%
D 2
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 4
25.0%
E 4
25.0%
A 2
12.5%
P 2
12.5%
T 2
12.5%
D 2
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 4
25.0%
E 4
25.0%
A 2
12.5%
P 2
12.5%
T 2
12.5%
D 2
12.5%

dateIdentified
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

identificationReferences
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

identificationVerificationStatus
Unsupported

Missing  Rejected  Unsupported 

Missing604622
Missing (%)> 99.9%
Memory size4.6 MiB

identificationRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing604622
Missing (%)> 99.9%
Memory size4.6 MiB

taxonID
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:51.469018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters48
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row2024-12-02T13:59:16.382Z
2nd row2024-12-02T13:59:48.546Z
ValueCountFrequency (%)
2024-12-02t13:59:16.382z 1
50.0%
2024-12-02t13:59:48.546z 1
50.0%
2025-01-07T10:41:51.573981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 9
18.8%
1 5
10.4%
0 4
8.3%
4 4
8.3%
- 4
8.3%
: 4
8.3%
5 3
 
6.2%
3 3
 
6.2%
T 2
 
4.2%
9 2
 
4.2%
Other values (4) 8
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 9
18.8%
1 5
10.4%
0 4
8.3%
4 4
8.3%
- 4
8.3%
: 4
8.3%
5 3
 
6.2%
3 3
 
6.2%
T 2
 
4.2%
9 2
 
4.2%
Other values (4) 8
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 9
18.8%
1 5
10.4%
0 4
8.3%
4 4
8.3%
- 4
8.3%
: 4
8.3%
5 3
 
6.2%
3 3
 
6.2%
T 2
 
4.2%
9 2
 
4.2%
Other values (4) 8
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 9
18.8%
1 5
10.4%
0 4
8.3%
4 4
8.3%
- 4
8.3%
: 4
8.3%
5 3
 
6.2%
3 3
 
6.2%
T 2
 
4.2%
9 2
 
4.2%
Other values (4) 8
16.7%

scientificNameID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

acceptedNameUsageID
Real number (ℝ)

Distinct188378
Distinct (%)31.4%
Missing4648
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3147779.704
Minimum1
Maximum12386548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:51.640461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1048343
Q11358413.75
median1660742
Q35038798.75
95-th percentile9429837.45
Maximum12386548
Range12386547
Interquartile range (IQR)3680385

Descriptive statistics

Standard deviation2782156.055
Coefficient of variation (CV)0.8838471292
Kurtosis0.9801945181
Mean3147779.704
Median Absolute Deviation (MAD)320464
Skewness1.423983392
Sum1.888598571 × 1012
Variance7.740392314 × 1012
MonotonicityNot monotonic
2025-01-07T10:41:51.711072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1340278 10672
 
1.8%
1340525 6265
 
1.0%
1340393 4073
 
0.7%
10409744 3623
 
0.6%
789 3466
 
0.6%
1340467 3343
 
0.6%
9164 3176
 
0.5%
1340350 3129
 
0.5%
1341979 2431
 
0.4%
1340485 2119
 
0.4%
Other values (188368) 557681
92.2%
(Missing) 4648
 
0.8%
ValueCountFrequency (%)
1 18
 
< 0.1%
54 6
 
< 0.1%
216 180
< 0.1%
360 11
 
< 0.1%
361 30
 
< 0.1%
ValueCountFrequency (%)
12386548 3
< 0.1%
12371456 3
< 0.1%
12370373 1
 
< 0.1%
12363953 5
< 0.1%
12356386 2
 
< 0.1%

parentNameUsageID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

originalNameUsageID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

nameAccordingToID
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

namePublishedInID
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:51.769579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length112
Median length80
Mean length80
Min length48

Characters and Unicode

Total characters160
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
2nd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED_FROM_COORDINATES
ValueCountFrequency (%)
occurrence_status_inferred_from_individual_count 1
50.0%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_derived_from_coordinates 1
50.0%
2025-01-07T10:41:51.884747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 16
10.0%
E 15
9.4%
R 13
 
8.1%
N 12
 
7.5%
D 12
 
7.5%
I 12
 
7.5%
T 11
 
6.9%
O 11
 
6.9%
C 11
 
6.9%
U 10
 
6.2%
Other values (11) 37
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 16
10.0%
E 15
9.4%
R 13
 
8.1%
N 12
 
7.5%
D 12
 
7.5%
I 12
 
7.5%
T 11
 
6.9%
O 11
 
6.9%
C 11
 
6.9%
U 10
 
6.2%
Other values (11) 37
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 16
10.0%
E 15
9.4%
R 13
 
8.1%
N 12
 
7.5%
D 12
 
7.5%
I 12
 
7.5%
T 11
 
6.9%
O 11
 
6.9%
C 11
 
6.9%
U 10
 
6.2%
Other values (11) 37
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 16
10.0%
E 15
9.4%
R 13
 
8.1%
N 12
 
7.5%
D 12
 
7.5%
I 12
 
7.5%
T 11
 
6.9%
O 11
 
6.9%
C 11
 
6.9%
U 10
 
6.2%
Other values (11) 37
23.1%

taxonConceptID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:51.929253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowStillImage
ValueCountFrequency (%)
stillimage 1
100.0%
2025-01-07T10:41:52.028484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 2
20.0%
S 1
10.0%
t 1
10.0%
i 1
10.0%
I 1
10.0%
m 1
10.0%
a 1
10.0%
g 1
10.0%
e 1
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2
20.0%
S 1
10.0%
t 1
10.0%
i 1
10.0%
I 1
10.0%
m 1
10.0%
a 1
10.0%
g 1
10.0%
e 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2
20.0%
S 1
10.0%
t 1
10.0%
i 1
10.0%
I 1
10.0%
m 1
10.0%
a 1
10.0%
g 1
10.0%
e 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2
20.0%
S 1
10.0%
t 1
10.0%
i 1
10.0%
I 1
10.0%
m 1
10.0%
a 1
10.0%
g 1
10.0%
e 1
10.0%
Distinct203338
Distinct (%)33.6%
Missing2
Missing (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:41:52.261445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length239
Median length108
Mean length31.31866747
Min length4

Characters and Unicode

Total characters18936018
Distinct characters109
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154758 ?
Unique (%)25.6%

Sample

1st rowCamponotus rufoglaucus var. rufigenis Forel
2nd rowAthrips mesoleuca Lower, 1900
3rd rowParanthrene asilipennis (Boisduval, 1832)
4th rowAcanthagrion trilobatum Leonard, 1977
5th rowCalathus nanulus Casey, 1920
ValueCountFrequency (%)
bombus 62365
 
2.7%
29343
 
1.3%
hagen 24881
 
1.1%
cresson 24121
 
1.0%
1861 19352
 
0.8%
fabricius 16608
 
0.7%
1863 16510
 
0.7%
selys 15944
 
0.7%
casey 15917
 
0.7%
latreille 15270
 
0.7%
Other values (119252) 2103492
89.7%
2025-01-07T10:41:52.571461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1739179
 
9.2%
a 1483659
 
7.8%
e 1211161
 
6.4%
i 1149549
 
6.1%
s 1059970
 
5.6%
r 959396
 
5.1%
o 891386
 
4.7%
l 793140
 
4.2%
n 766475
 
4.0%
1 670618
 
3.5%
Other values (99) 8211485
43.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18936018
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1739179
 
9.2%
a 1483659
 
7.8%
e 1211161
 
6.4%
i 1149549
 
6.1%
s 1059970
 
5.6%
r 959396
 
5.1%
o 891386
 
4.7%
l 793140
 
4.2%
n 766475
 
4.0%
1 670618
 
3.5%
Other values (99) 8211485
43.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18936018
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1739179
 
9.2%
a 1483659
 
7.8%
e 1211161
 
6.4%
i 1149549
 
6.1%
s 1059970
 
5.6%
r 959396
 
5.1%
o 891386
 
4.7%
l 793140
 
4.2%
n 766475
 
4.0%
1 670618
 
3.5%
Other values (99) 8211485
43.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18936018
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1739179
 
9.2%
a 1483659
 
7.8%
e 1211161
 
6.4%
i 1149549
 
6.1%
s 1059970
 
5.6%
r 959396
 
5.1%
o 891386
 
4.7%
l 793140
 
4.2%
n 766475
 
4.0%
1 670618
 
3.5%
Other values (99) 8211485
43.4%

acceptedNameUsage
Boolean

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
False
 
2
(Missing)
604624 
ValueCountFrequency (%)
False 2
 
< 0.1%
(Missing) 604624
> 99.9%
2025-01-07T10:41:52.662253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

parentNameUsage
Unsupported

Missing  Rejected  Unsupported 

Missing604623
Missing (%)> 99.9%
Memory size4.6 MiB

originalNameUsage
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1680860.5
Minimum1424710
Maximum1937011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:52.703692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1424710
5-th percentile1450325.05
Q11552785.25
median1680860.5
Q31808935.75
95-th percentile1911395.95
Maximum1937011
Range512301
Interquartile range (IQR)256150.5

Descriptive statistics

Standard deviation362251.5111
Coefficient of variation (CV)0.2155155119
Kurtosisnan
Mean1680860.5
Median Absolute Deviation (MAD)256150.5
Skewnessnan
Sum3361721
Variance1.312261573 × 1011
MonotonicityStrictly decreasing
2025-01-07T10:41:52.751167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1937011 1
 
< 0.1%
1424710 1
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
1424710 1
< 0.1%
1937011 1
< 0.1%
ValueCountFrequency (%)
1937011 1
< 0.1%
1424710 1
< 0.1%

nameAccordingTo
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:52.796440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosisnan
Mean1
Median Absolute Deviation (MAD)0
Skewnessnan
Sum2
Variance0
MonotonicityIncreasing
2025-01-07T10:41:52.838947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 2
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
1 2
< 0.1%
ValueCountFrequency (%)
1 2
< 0.1%

namePublishedIn
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean54
Minimum54
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:52.882428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile54
Q154
median54
Q354
95-th percentile54
Maximum54
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosisnan
Mean54
Median Absolute Deviation (MAD)0
Skewnessnan
Sum108
Variance0
MonotonicityIncreasing
2025-01-07T10:41:52.926934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
54 2
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
54 2
< 0.1%
ValueCountFrequency (%)
54 2
< 0.1%

namePublishedInYear
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean216
Minimum216
Maximum216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:52.969934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum216
5-th percentile216
Q1216
median216
Q3216
95-th percentile216
Maximum216
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosisnan
Mean216
Median Absolute Deviation (MAD)0
Skewnessnan
Sum432
Variance0
MonotonicityIncreasing
2025-01-07T10:41:53.014175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
216 2
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
216 2
< 0.1%
ValueCountFrequency (%)
216 2
< 0.1%
Distinct3456
Distinct (%)0.6%
Missing4647
Missing (%)0.8%
Memory size4.6 MiB
2025-01-07T10:41:53.150253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length97
Median length91
Mean length62.39093868
Min length3

Characters and Unicode

Total characters37433253
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique577 ?
Unique (%)0.1%

Sample

1st rowAnimalia, Arthropoda, Insecta, Hymenoptera, Formicidae, Formicinae
2nd rowAnimalia, Arthropoda, Insecta, Lepidoptera, Gelechiidae, Gelechiinae
3rd rowAnimalia, Arthropoda, Insecta, Lepidoptera, Sesiidae, Sesiinae
4th rowAnimalia, Arthropoda, Insecta, Odonata, Zygoptera, Coenagrionidae
5th rowAnimalia, Arthropoda, Insecta, Coleoptera, Carabidae
ValueCountFrequency (%)
arthropoda 599697
17.3%
animalia 598328
17.3%
insecta 587915
17.0%
hymenoptera 146500
 
4.2%
odonata 117281
 
3.4%
lepidoptera 99941
 
2.9%
apidae 82932
 
2.4%
diptera 73535
 
2.1%
coleoptera 72078
 
2.1%
apinae 63521
 
1.8%
Other values (2938) 1026036
29.6%
2025-01-07T10:41:53.376656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4570771
12.2%
e 2938293
 
7.8%
2867785
 
7.7%
, 2867419
 
7.7%
i 2865053
 
7.7%
o 2432895
 
6.5%
r 2316840
 
6.2%
t 2192044
 
5.9%
n 2160053
 
5.8%
p 1690137
 
4.5%
Other values (53) 10531963
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37433253
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4570771
12.2%
e 2938293
 
7.8%
2867785
 
7.7%
, 2867419
 
7.7%
i 2865053
 
7.7%
o 2432895
 
6.5%
r 2316840
 
6.2%
t 2192044
 
5.9%
n 2160053
 
5.8%
p 1690137
 
4.5%
Other values (53) 10531963
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37433253
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4570771
12.2%
e 2938293
 
7.8%
2867785
 
7.7%
, 2867419
 
7.7%
i 2865053
 
7.7%
o 2432895
 
6.5%
r 2316840
 
6.2%
t 2192044
 
5.9%
n 2160053
 
5.8%
p 1690137
 
4.5%
Other values (53) 10531963
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37433253
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4570771
12.2%
e 2938293
 
7.8%
2867785
 
7.7%
, 2867419
 
7.7%
i 2865053
 
7.7%
o 2432895
 
6.5%
r 2316840
 
6.2%
t 2192044
 
5.9%
n 2160053
 
5.8%
p 1690137
 
4.5%
Other values (53) 10531963
28.1%
Distinct4
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:41:53.434434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.046071608
Min length4

Characters and Unicode

Total characters4864848
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAnimalia
2nd rowAnimalia
3rd rowAnimalia
4th rowAnimalia
5th rowAnimalia
ValueCountFrequency (%)
animalia 599978
98.5%
incertae 4644
 
0.8%
sedis 4644
 
0.8%
9417 1
 
< 0.1%
4209 1
 
< 0.1%
2025-01-07T10:41:53.538510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 1209244
24.9%
a 1204600
24.8%
n 604622
12.4%
A 599978
12.3%
m 599978
12.3%
l 599978
12.3%
e 13932
 
0.3%
s 9288
 
0.2%
c 4644
 
0.1%
t 4644
 
0.1%
Other values (9) 13940
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4864848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1209244
24.9%
a 1204600
24.8%
n 604622
12.4%
A 599978
12.3%
m 599978
12.3%
l 599978
12.3%
e 13932
 
0.3%
s 9288
 
0.2%
c 4644
 
0.1%
t 4644
 
0.1%
Other values (9) 13940
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4864848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1209244
24.9%
a 1204600
24.8%
n 604622
12.4%
A 599978
12.3%
m 599978
12.3%
l 599978
12.3%
e 13932
 
0.3%
s 9288
 
0.2%
c 4644
 
0.1%
t 4644
 
0.1%
Other values (9) 13940
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4864848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1209244
24.9%
a 1204600
24.8%
n 604622
12.4%
A 599978
12.3%
m 599978
12.3%
l 599978
12.3%
e 13932
 
0.3%
s 9288
 
0.2%
c 4644
 
0.1%
t 4644
 
0.1%
Other values (9) 13940
 
0.3%

phylum
Text

Distinct9
Distinct (%)< 0.1%
Missing5245
Missing (%)0.9%
Memory size4.6 MiB
2025-01-07T10:41:53.589809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length10
Mean length9.999918249
Min length7

Characters and Unicode

Total characters5993761
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowArthropoda
2nd rowArthropoda
3rd rowArthropoda
4th rowArthropoda
5th rowArthropoda
ValueCountFrequency (%)
arthropoda 599346
> 99.9%
cnidaria 18
 
< 0.1%
onychophora 6
 
< 0.1%
mollusca 5
 
< 0.1%
chordata 2
 
< 0.1%
1936987 1
 
< 0.1%
nemertea 1
 
< 0.1%
1424684 1
 
< 0.1%
echinodermata 1
 
< 0.1%
2025-01-07T10:41:53.702067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 1198720
20.0%
o 1198712
20.0%
a 599400
10.0%
d 599367
10.0%
h 599361
10.0%
p 599352
10.0%
t 599350
10.0%
A 599346
10.0%
i 37
 
< 0.1%
n 25
 
< 0.1%
Other values (20) 91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5993761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1198720
20.0%
o 1198712
20.0%
a 599400
10.0%
d 599367
10.0%
h 599361
10.0%
p 599352
10.0%
t 599350
10.0%
A 599346
10.0%
i 37
 
< 0.1%
n 25
 
< 0.1%
Other values (20) 91
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5993761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1198720
20.0%
o 1198712
20.0%
a 599400
10.0%
d 599367
10.0%
h 599361
10.0%
p 599352
10.0%
t 599350
10.0%
A 599346
10.0%
i 37
 
< 0.1%
n 25
 
< 0.1%
Other values (20) 91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5993761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1198720
20.0%
o 1198712
20.0%
a 599400
10.0%
d 599367
10.0%
h 599361
10.0%
p 599352
10.0%
t 599350
10.0%
A 599346
10.0%
i 37
 
< 0.1%
n 25
 
< 0.1%
Other values (20) 91
 
< 0.1%

class
Text

Distinct13
Distinct (%)< 0.1%
Missing5283
Missing (%)0.9%
Memory size4.6 MiB
2025-01-07T10:41:53.750571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length7
Mean length7.038410393
Min length7

Characters and Unicode

Total characters4218422
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowInsecta
2nd rowInsecta
3rd rowInsecta
4th rowInsecta
5th rowInsecta
ValueCountFrequency (%)
insecta 588111
98.1%
arachnida 7917
 
1.3%
diplopoda 1599
 
0.3%
collembola 820
 
0.1%
chilopoda 736
 
0.1%
diplura 77
 
< 0.1%
protura 62
 
< 0.1%
symphyla 8
 
< 0.1%
malacostraca 5
 
< 0.1%
pauropoda 4
 
< 0.1%
Other values (3) 4
 
< 0.1%
2025-01-07T10:41:53.862563image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 607282
14.4%
c 596040
14.1%
n 596030
14.1%
e 588932
14.0%
t 588180
13.9%
s 588119
13.9%
I 588111
13.9%
i 10333
 
0.2%
d 10259
 
0.2%
h 8663
 
0.2%
Other values (16) 36473
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4218422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 607282
14.4%
c 596040
14.1%
n 596030
14.1%
e 588932
14.0%
t 588180
13.9%
s 588119
13.9%
I 588111
13.9%
i 10333
 
0.2%
d 10259
 
0.2%
h 8663
 
0.2%
Other values (16) 36473
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4218422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 607282
14.4%
c 596040
14.1%
n 596030
14.1%
e 588932
14.0%
t 588180
13.9%
s 588119
13.9%
I 588111
13.9%
i 10333
 
0.2%
d 10259
 
0.2%
h 8663
 
0.2%
Other values (16) 36473
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4218422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 607282
14.4%
c 596040
14.1%
n 596030
14.1%
e 588932
14.0%
t 588180
13.9%
s 588119
13.9%
I 588111
13.9%
i 10333
 
0.2%
d 10259
 
0.2%
h 8663
 
0.2%
Other values (16) 36473
 
0.9%

order
Text

Distinct74
Distinct (%)< 0.1%
Missing5577
Missing (%)0.9%
Memory size4.6 MiB
2025-01-07T10:41:53.946389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length9.451483935
Min length6

Characters and Unicode

Total characters5661902
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowHymenoptera
2nd rowLepidoptera
3rd rowLepidoptera
4th rowOdonata
5th rowColeoptera
ValueCountFrequency (%)
hymenoptera 146330
24.4%
odonata 117284
19.6%
lepidoptera 99491
16.6%
diptera 73566
12.3%
coleoptera 71961
12.0%
hemiptera 37757
 
6.3%
siphonaptera 10087
 
1.7%
trichoptera 9104
 
1.5%
thysanoptera 4628
 
0.8%
araneae 4624
 
0.8%
Other values (64) 24217
 
4.0%
2025-01-07T10:41:54.078887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 849733
15.0%
a 742019
13.1%
t 591304
10.4%
p 577797
10.2%
o 563778
10.0%
r 489265
8.6%
n 284180
 
5.0%
i 238281
 
4.2%
d 228781
 
4.0%
m 192363
 
3.4%
Other values (37) 904401
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5661902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 849733
15.0%
a 742019
13.1%
t 591304
10.4%
p 577797
10.2%
o 563778
10.0%
r 489265
8.6%
n 284180
 
5.0%
i 238281
 
4.2%
d 228781
 
4.0%
m 192363
 
3.4%
Other values (37) 904401
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5661902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 849733
15.0%
a 742019
13.1%
t 591304
10.4%
p 577797
10.2%
o 563778
10.0%
r 489265
8.6%
n 284180
 
5.0%
i 238281
 
4.2%
d 228781
 
4.0%
m 192363
 
3.4%
Other values (37) 904401
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5661902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 849733
15.0%
a 742019
13.1%
t 591304
10.4%
p 577797
10.2%
o 563778
10.0%
r 489265
8.6%
n 284180
 
5.0%
i 238281
 
4.2%
d 228781
 
4.0%
m 192363
 
3.4%
Other values (37) 904401
16.0%

superfamily
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:54.135132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length19.5
Mean length19.5
Min length17

Characters and Unicode

Total characters39
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowTroides amphrysus
2nd rowGynacantha membranalis
ValueCountFrequency (%)
troides 1
25.0%
amphrysus 1
25.0%
gynacantha 1
25.0%
membranalis 1
25.0%
2025-01-07T10:41:54.242934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6
15.4%
s 4
 
10.3%
r 3
 
7.7%
m 3
 
7.7%
n 3
 
7.7%
h 2
 
5.1%
i 2
 
5.1%
2
 
5.1%
y 2
 
5.1%
e 2
 
5.1%
Other values (10) 10
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6
15.4%
s 4
 
10.3%
r 3
 
7.7%
m 3
 
7.7%
n 3
 
7.7%
h 2
 
5.1%
i 2
 
5.1%
2
 
5.1%
y 2
 
5.1%
e 2
 
5.1%
Other values (10) 10
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6
15.4%
s 4
 
10.3%
r 3
 
7.7%
m 3
 
7.7%
n 3
 
7.7%
h 2
 
5.1%
i 2
 
5.1%
2
 
5.1%
y 2
 
5.1%
e 2
 
5.1%
Other values (10) 10
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6
15.4%
s 4
 
10.3%
r 3
 
7.7%
m 3
 
7.7%
n 3
 
7.7%
h 2
 
5.1%
i 2
 
5.1%
2
 
5.1%
y 2
 
5.1%
e 2
 
5.1%
Other values (10) 10
25.6%

family
Text

Missing 

Distinct1494
Distinct (%)0.3%
Missing11642
Missing (%)1.9%
Memory size4.6 MiB
2025-01-07T10:41:54.405317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length35
Median length21
Mean length10.49803873
Min length6

Characters and Unicode

Total characters6225169
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique196 ?
Unique (%)< 0.1%

Sample

1st rowFormicidae
2nd rowGelechiidae
3rd rowSesiidae
4th rowCoenagrionidae
5th rowCarabidae
ValueCountFrequency (%)
apidae 82646
 
13.9%
libellulidae 42503
 
7.2%
coenagrionidae 36255
 
6.1%
chrysomelidae 17448
 
2.9%
crambidae 13614
 
2.3%
asilidae 13374
 
2.3%
geometridae 12793
 
2.2%
psychodidae 11788
 
2.0%
curculionidae 11689
 
2.0%
formicidae 9878
 
1.7%
Other values (1490) 341002
57.5%
2025-01-07T10:41:54.769872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 902858
14.5%
e 876852
14.1%
a 817696
13.1%
d 657115
10.6%
o 322939
 
5.2%
l 317031
 
5.1%
r 285013
 
4.6%
p 208767
 
3.4%
n 202426
 
3.3%
h 150237
 
2.4%
Other values (50) 1484235
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6225169
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 902858
14.5%
e 876852
14.1%
a 817696
13.1%
d 657115
10.6%
o 322939
 
5.2%
l 317031
 
5.1%
r 285013
 
4.6%
p 208767
 
3.4%
n 202426
 
3.3%
h 150237
 
2.4%
Other values (50) 1484235
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6225169
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 902858
14.5%
e 876852
14.1%
a 817696
13.1%
d 657115
10.6%
o 322939
 
5.2%
l 317031
 
5.1%
r 285013
 
4.6%
p 208767
 
3.4%
n 202426
 
3.3%
h 150237
 
2.4%
Other values (50) 1484235
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6225169
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 902858
14.5%
e 876852
14.1%
a 817696
13.1%
d 657115
10.6%
o 322939
 
5.2%
l 317031
 
5.1%
r 285013
 
4.6%
p 208767
 
3.4%
n 202426
 
3.3%
h 150237
 
2.4%
Other values (50) 1484235
23.8%

subfamily
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:54.834050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length19.5
Mean length19.5
Min length17

Characters and Unicode

Total characters39
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowTroides amphrysus
2nd rowGynacantha membranalis
ValueCountFrequency (%)
troides 1
25.0%
amphrysus 1
25.0%
gynacantha 1
25.0%
membranalis 1
25.0%
2025-01-07T10:41:54.945376image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6
15.4%
s 4
 
10.3%
r 3
 
7.7%
m 3
 
7.7%
n 3
 
7.7%
h 2
 
5.1%
i 2
 
5.1%
2
 
5.1%
y 2
 
5.1%
e 2
 
5.1%
Other values (10) 10
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6
15.4%
s 4
 
10.3%
r 3
 
7.7%
m 3
 
7.7%
n 3
 
7.7%
h 2
 
5.1%
i 2
 
5.1%
2
 
5.1%
y 2
 
5.1%
e 2
 
5.1%
Other values (10) 10
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6
15.4%
s 4
 
10.3%
r 3
 
7.7%
m 3
 
7.7%
n 3
 
7.7%
h 2
 
5.1%
i 2
 
5.1%
2
 
5.1%
y 2
 
5.1%
e 2
 
5.1%
Other values (10) 10
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6
15.4%
s 4
 
10.3%
r 3
 
7.7%
m 3
 
7.7%
n 3
 
7.7%
h 2
 
5.1%
i 2
 
5.1%
2
 
5.1%
y 2
 
5.1%
e 2
 
5.1%
Other values (10) 10
25.6%

tribe
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

subtribe
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:54.990057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEML
2nd rowEML
ValueCountFrequency (%)
eml 2
100.0%
2025-01-07T10:41:55.085939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2
33.3%
M 2
33.3%
L 2
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2
33.3%
M 2
33.3%
L 2
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2
33.3%
M 2
33.3%
L 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2
33.3%
M 2
33.3%
L 2
33.3%

genus
Text

Missing 

Distinct35722
Distinct (%)6.1%
Missing19883
Missing (%)3.3%
Memory size4.6 MiB
2025-01-07T10:41:55.296293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length19
Mean length8.97094279
Min length3

Characters and Unicode

Total characters5245696
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11794 ?
Unique (%)2.0%

Sample

1st rowCamponotus
2nd rowAthrips
3rd rowParanthrene
4th rowAcanthagrion
5th rowCalathus
ValueCountFrequency (%)
bombus 62386
 
10.7%
xylocopa 11739
 
2.0%
argia 8660
 
1.5%
enallagma 7903
 
1.4%
crambus 7885
 
1.3%
ischnura 7465
 
1.3%
sympetrum 6026
 
1.0%
apis 4967
 
0.8%
erythrodiplax 4175
 
0.7%
lestes 4149
 
0.7%
Other values (35712) 459388
78.6%
2025-01-07T10:41:55.575130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 535407
 
10.2%
o 472261
 
9.0%
s 396292
 
7.6%
i 368556
 
7.0%
e 354889
 
6.8%
r 324058
 
6.2%
l 257744
 
4.9%
u 248449
 
4.7%
t 231309
 
4.4%
m 228883
 
4.4%
Other values (54) 1827848
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5245696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 535407
 
10.2%
o 472261
 
9.0%
s 396292
 
7.6%
i 368556
 
7.0%
e 354889
 
6.8%
r 324058
 
6.2%
l 257744
 
4.9%
u 248449
 
4.7%
t 231309
 
4.4%
m 228883
 
4.4%
Other values (54) 1827848
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5245696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 535407
 
10.2%
o 472261
 
9.0%
s 396292
 
7.6%
i 368556
 
7.0%
e 354889
 
6.8%
r 324058
 
6.2%
l 257744
 
4.9%
u 248449
 
4.7%
t 231309
 
4.4%
m 228883
 
4.4%
Other values (54) 1827848
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5245696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 535407
 
10.2%
o 472261
 
9.0%
s 396292
 
7.6%
i 368556
 
7.0%
e 354889
 
6.8%
r 324058
 
6.2%
l 257744
 
4.9%
u 248449
 
4.7%
t 231309
 
4.4%
m 228883
 
4.4%
Other values (54) 1827848
34.8%

genericName
Text

Missing 

Distinct38103
Distinct (%)6.5%
Missing19882
Missing (%)3.3%
Memory size4.6 MiB
2025-01-07T10:41:55.790954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length19
Mean length8.918990191
Min length1

Characters and Unicode

Total characters5215326
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13468 ?
Unique (%)2.3%

Sample

1st rowCamponotus
2nd rowAthrips
3rd rowParanthrene
4th rowAcanthagrion
5th rowCalathus
ValueCountFrequency (%)
bombus 62365
 
10.7%
xylocopa 11743
 
2.0%
argia 8660
 
1.5%
enallagma 7977
 
1.4%
crambus 7970
 
1.4%
ischnura 7456
 
1.3%
sympetrum 6028
 
1.0%
apis 4968
 
0.8%
lestes 4235
 
0.7%
erythrodiplax 4175
 
0.7%
Other values (38093) 459167
78.5%
2025-01-07T10:41:56.067366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 528684
 
10.1%
o 470494
 
9.0%
s 396333
 
7.6%
i 366131
 
7.0%
e 352590
 
6.8%
r 320159
 
6.1%
l 255087
 
4.9%
u 247647
 
4.7%
m 230840
 
4.4%
t 230398
 
4.4%
Other values (55) 1816963
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5215326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 528684
 
10.1%
o 470494
 
9.0%
s 396333
 
7.6%
i 366131
 
7.0%
e 352590
 
6.8%
r 320159
 
6.1%
l 255087
 
4.9%
u 247647
 
4.7%
m 230840
 
4.4%
t 230398
 
4.4%
Other values (55) 1816963
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5215326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 528684
 
10.1%
o 470494
 
9.0%
s 396333
 
7.6%
i 366131
 
7.0%
e 352590
 
6.8%
r 320159
 
6.1%
l 255087
 
4.9%
u 247647
 
4.7%
m 230840
 
4.4%
t 230398
 
4.4%
Other values (55) 1816963
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5215326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 528684
 
10.1%
o 470494
 
9.0%
s 396333
 
7.6%
i 366131
 
7.0%
e 352590
 
6.8%
r 320159
 
6.1%
l 255087
 
4.9%
u 247647
 
4.7%
m 230840
 
4.4%
t 230398
 
4.4%
Other values (55) 1816963
34.8%

subgenus
Boolean

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
True
 
2
(Missing)
604624 
ValueCountFrequency (%)
True 2
 
< 0.1%
(Missing) 604624
> 99.9%
2025-01-07T10:41:56.137150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

infragenericEpithet
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

specificEpithet
Text

Missing 

Distinct74464
Distinct (%)15.0%
Missing109508
Missing (%)18.1%
Memory size4.6 MiB
2025-01-07T10:41:56.321038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length19
Mean length8.680070205
Min length2

Characters and Unicode

Total characters4297659
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40224 ?
Unique (%)8.1%

Sample

1st rowrufoglaucus
2nd rowmesoleuca
3rd rowasilipennis
4th rowtrilobatum
5th rownanulus
ValueCountFrequency (%)
sylvicola 6282
 
1.3%
bifarius 4077
 
0.8%
kirbyellus 3621
 
0.7%
flavifrons 3474
 
0.7%
impatiens 3132
 
0.6%
nevadensis 2510
 
0.5%
cerana 2431
 
0.5%
affinis 2243
 
0.5%
mixtus 2136
 
0.4%
bimaculatus 2025
 
0.4%
Other values (74454) 463187
93.6%
2025-01-07T10:41:56.604834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 563643
13.1%
i 497353
11.6%
s 385336
 
9.0%
e 331379
 
7.7%
l 295576
 
6.9%
n 285391
 
6.6%
r 268158
 
6.2%
u 259788
 
6.0%
t 231478
 
5.4%
c 208825
 
4.9%
Other values (22) 970732
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4297659
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 563643
13.1%
i 497353
11.6%
s 385336
 
9.0%
e 331379
 
7.7%
l 295576
 
6.9%
n 285391
 
6.6%
r 268158
 
6.2%
u 259788
 
6.0%
t 231478
 
5.4%
c 208825
 
4.9%
Other values (22) 970732
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4297659
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 563643
13.1%
i 497353
11.6%
s 385336
 
9.0%
e 331379
 
7.7%
l 295576
 
6.9%
n 285391
 
6.6%
r 268158
 
6.2%
u 259788
 
6.0%
t 231478
 
5.4%
c 208825
 
4.9%
Other values (22) 970732
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4297659
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 563643
13.1%
i 497353
11.6%
s 385336
 
9.0%
e 331379
 
7.7%
l 295576
 
6.9%
n 285391
 
6.6%
r 268158
 
6.2%
u 259788
 
6.0%
t 231478
 
5.4%
c 208825
 
4.9%
Other values (22) 970732
22.6%

infraspecificEpithet
Text

Missing 

Distinct4964
Distinct (%)27.2%
Missing586367
Missing (%)97.0%
Memory size4.6 MiB
2025-01-07T10:41:56.809975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length17
Mean length8.306752834
Min length3

Characters and Unicode

Total characters151673
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3559 ?
Unique (%)19.5%

Sample

1st rowrufigenis
2nd rowmarianae
3rd rowneglectum
4th rowlavatus
5th rowfloridensis
ValueCountFrequency (%)
violacea 979
 
5.4%
vagans 869
 
4.8%
portia 724
 
4.0%
auricomus 587
 
3.2%
virginica 587
 
3.2%
dorsata 437
 
2.4%
arizonensis 431
 
2.4%
bantorum 320
 
1.8%
binghami 303
 
1.7%
californica 291
 
1.6%
Other values (4954) 12731
69.7%
2025-01-07T10:41:57.084077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 22661
14.9%
i 18593
12.3%
s 12613
 
8.3%
n 10870
 
7.2%
r 10424
 
6.9%
e 9656
 
6.4%
o 9202
 
6.1%
c 7875
 
5.2%
u 7729
 
5.1%
l 7489
 
4.9%
Other values (17) 34561
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 151673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 22661
14.9%
i 18593
12.3%
s 12613
 
8.3%
n 10870
 
7.2%
r 10424
 
6.9%
e 9656
 
6.4%
o 9202
 
6.1%
c 7875
 
5.2%
u 7729
 
5.1%
l 7489
 
4.9%
Other values (17) 34561
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 151673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 22661
14.9%
i 18593
12.3%
s 12613
 
8.3%
n 10870
 
7.2%
r 10424
 
6.9%
e 9656
 
6.4%
o 9202
 
6.1%
c 7875
 
5.2%
u 7729
 
5.1%
l 7489
 
4.9%
Other values (17) 34561
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 151673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 22661
14.9%
i 18593
12.3%
s 12613
 
8.3%
n 10870
 
7.2%
r 10424
 
6.9%
e 9656
 
6.4%
o 9202
 
6.1%
c 7875
 
5.2%
u 7729
 
5.1%
l 7489
 
4.9%
Other values (17) 34561
22.8%

cultivarEpithet
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:57.142585image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length8.5
Mean length8.5
Min length4

Characters and Unicode

Total characters17
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowASIA
2nd rowLATIN_AMERICA
ValueCountFrequency (%)
asia 1
50.0%
latin_america 1
50.0%
2025-01-07T10:41:57.237796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 5
29.4%
I 3
17.6%
S 1
 
5.9%
L 1
 
5.9%
T 1
 
5.9%
N 1
 
5.9%
_ 1
 
5.9%
M 1
 
5.9%
E 1
 
5.9%
R 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5
29.4%
I 3
17.6%
S 1
 
5.9%
L 1
 
5.9%
T 1
 
5.9%
N 1
 
5.9%
_ 1
 
5.9%
M 1
 
5.9%
E 1
 
5.9%
R 1
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5
29.4%
I 3
17.6%
S 1
 
5.9%
L 1
 
5.9%
T 1
 
5.9%
N 1
 
5.9%
_ 1
 
5.9%
M 1
 
5.9%
E 1
 
5.9%
R 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5
29.4%
I 3
17.6%
S 1
 
5.9%
L 1
 
5.9%
T 1
 
5.9%
N 1
 
5.9%
_ 1
 
5.9%
M 1
 
5.9%
E 1
 
5.9%
R 1
 
5.9%
Distinct12
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:41:57.286492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length7
Mean length6.758805472
Min length4

Characters and Unicode

Total characters4086536
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVARIETY
2nd rowSPECIES
3rd rowSPECIES
4th rowSPECIES
5th rowSPECIES
ValueCountFrequency (%)
species 476863
78.9%
genus 89611
 
14.8%
subspecies 17825
 
2.9%
family 10445
 
1.7%
kingdom 4662
 
0.8%
order 4514
 
0.7%
variety 391
 
0.1%
class 253
 
< 0.1%
form 41
 
< 0.1%
unranked 11
 
< 0.1%
Other values (2) 8
 
< 0.1%
2025-01-07T10:41:57.387042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 1097318
26.9%
E 1083905
26.5%
I 510188
12.5%
C 494943
12.1%
P 494694
12.1%
U 107453
 
2.6%
N 94297
 
2.3%
G 94273
 
2.3%
B 17825
 
0.4%
M 15156
 
0.4%
Other values (12) 76484
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4086536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1097318
26.9%
E 1083905
26.5%
I 510188
12.5%
C 494943
12.1%
P 494694
12.1%
U 107453
 
2.6%
N 94297
 
2.3%
G 94273
 
2.3%
B 17825
 
0.4%
M 15156
 
0.4%
Other values (12) 76484
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4086536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1097318
26.9%
E 1083905
26.5%
I 510188
12.5%
C 494943
12.1%
P 494694
12.1%
U 107453
 
2.6%
N 94297
 
2.3%
G 94273
 
2.3%
B 17825
 
0.4%
M 15156
 
0.4%
Other values (12) 76484
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4086536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1097318
26.9%
E 1083905
26.5%
I 510188
12.5%
C 494943
12.1%
P 494694
12.1%
U 107453
 
2.6%
N 94297
 
2.3%
G 94273
 
2.3%
B 17825
 
0.4%
M 15156
 
0.4%
Other values (12) 76484
 
1.9%

verbatimTaxonRank
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:57.429420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPER
ValueCountFrequency (%)
per 1
100.0%
2025-01-07T10:41:57.519407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 1
33.3%
E 1
33.3%
R 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 1
33.3%
E 1
33.3%
R 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 1
33.3%
E 1
33.3%
R 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 1
33.3%
E 1
33.3%
R 1
33.3%

vernacularName
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:57.562406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowTYPE
2nd rowPeru
ValueCountFrequency (%)
type 1
50.0%
peru 1
50.0%
2025-01-07T10:41:57.658088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 2
25.0%
T 1
12.5%
Y 1
12.5%
E 1
12.5%
e 1
12.5%
r 1
12.5%
u 1
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 2
25.0%
T 1
12.5%
Y 1
12.5%
E 1
12.5%
e 1
12.5%
r 1
12.5%
u 1
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 2
25.0%
T 1
12.5%
Y 1
12.5%
E 1
12.5%
e 1
12.5%
r 1
12.5%
u 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 2
25.0%
T 1
12.5%
Y 1
12.5%
E 1
12.5%
e 1
12.5%
r 1
12.5%
u 1
12.5%

nomenclaturalCode
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:57.699490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPER.16_1
ValueCountFrequency (%)
per.16_1 1
100.0%
2025-01-07T10:41:57.790748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2
25.0%
E 1
12.5%
P 1
12.5%
R 1
12.5%
. 1
12.5%
6 1
12.5%
_ 1
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2
25.0%
E 1
12.5%
P 1
12.5%
R 1
12.5%
. 1
12.5%
6 1
12.5%
_ 1
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2
25.0%
E 1
12.5%
P 1
12.5%
R 1
12.5%
. 1
12.5%
6 1
12.5%
_ 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2
25.0%
E 1
12.5%
P 1
12.5%
R 1
12.5%
. 1
12.5%
6 1
12.5%
_ 1
12.5%
Distinct4
Distinct (%)< 0.1%
Missing4647
Missing (%)0.8%
Memory size4.6 MiB
2025-01-07T10:41:57.834258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.880410814
Min length4

Characters and Unicode

Total characters4728081
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowACCEPTED
2nd rowACCEPTED
3rd rowACCEPTED
4th rowACCEPTED
5th rowSYNONYM
ValueCountFrequency (%)
accepted 518943
86.5%
synonym 71747
 
12.0%
doubtful 9288
 
1.5%
lima 1
 
< 0.1%
2025-01-07T10:41:57.948954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 1037886
22.0%
E 1037886
22.0%
D 528231
11.2%
T 528231
11.2%
A 518943
11.0%
P 518943
11.0%
Y 143494
 
3.0%
N 143494
 
3.0%
O 81035
 
1.7%
S 71747
 
1.5%
Other values (8) 118191
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4728081
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1037886
22.0%
E 1037886
22.0%
D 528231
11.2%
T 528231
11.2%
A 518943
11.0%
P 518943
11.0%
Y 143494
 
3.0%
N 143494
 
3.0%
O 81035
 
1.7%
S 71747
 
1.5%
Other values (8) 118191
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4728081
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1037886
22.0%
E 1037886
22.0%
D 528231
11.2%
T 528231
11.2%
A 518943
11.0%
P 518943
11.0%
Y 143494
 
3.0%
N 143494
 
3.0%
O 81035
 
1.7%
S 71747
 
1.5%
Other values (8) 118191
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4728081
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1037886
22.0%
E 1037886
22.0%
D 528231
11.2%
T 528231
11.2%
A 518943
11.0%
P 518943
11.0%
Y 143494
 
3.0%
N 143494
 
3.0%
O 81035
 
1.7%
S 71747
 
1.5%
Other values (8) 118191
 
2.5%

nomenclaturalStatus
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:57.993955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPER.16.6_1
ValueCountFrequency (%)
per.16.6_1 1
100.0%
2025-01-07T10:41:58.090698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 2
20.0%
1 2
20.0%
. 2
20.0%
R 1
10.0%
E 1
10.0%
P 1
10.0%
_ 1
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 2
20.0%
1 2
20.0%
. 2
20.0%
R 1
10.0%
E 1
10.0%
P 1
10.0%
_ 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 2
20.0%
1 2
20.0%
. 2
20.0%
R 1
10.0%
E 1
10.0%
P 1
10.0%
_ 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 2
20.0%
1 2
20.0%
. 2
20.0%
R 1
10.0%
E 1
10.0%
P 1
10.0%
_ 1
10.0%

taxonRemarks
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:41:58.133795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowHuarochiri
ValueCountFrequency (%)
huarochiri 1
100.0%
2025-01-07T10:41:58.226635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 2
20.0%
i 2
20.0%
u 1
10.0%
H 1
10.0%
a 1
10.0%
o 1
10.0%
c 1
10.0%
h 1
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2
20.0%
i 2
20.0%
u 1
10.0%
H 1
10.0%
a 1
10.0%
o 1
10.0%
c 1
10.0%
h 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2
20.0%
i 2
20.0%
u 1
10.0%
H 1
10.0%
a 1
10.0%
o 1
10.0%
c 1
10.0%
h 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2
20.0%
i 2
20.0%
u 1
10.0%
H 1
10.0%
a 1
10.0%
o 1
10.0%
c 1
10.0%
h 1
10.0%
Distinct2
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:41:58.282634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length35.99996196
Min length13

Characters and Unicode

Total characters21766405
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row821cc27a-e3bb-4bc5-ac34-89ada245069d
2nd row821cc27a-e3bb-4bc5-ac34-89ada245069d
3rd row821cc27a-e3bb-4bc5-ac34-89ada245069d
4th row821cc27a-e3bb-4bc5-ac34-89ada245069d
5th row821cc27a-e3bb-4bc5-ac34-89ada245069d
ValueCountFrequency (%)
821cc27a-e3bb-4bc5-ac34-89ada245069d 604622
> 99.9%
per.16.6.16_1 1
 
< 0.1%
2025-01-07T10:41:58.387545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 2418488
11.1%
- 2418488
11.1%
a 2418488
11.1%
2 1813866
8.3%
4 1813866
8.3%
b 1813866
8.3%
5 1209244
 
5.6%
8 1209244
 
5.6%
3 1209244
 
5.6%
9 1209244
 
5.6%
Other values (11) 4232367
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21766405
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 2418488
11.1%
- 2418488
11.1%
a 2418488
11.1%
2 1813866
8.3%
4 1813866
8.3%
b 1813866
8.3%
5 1209244
 
5.6%
8 1209244
 
5.6%
3 1209244
 
5.6%
9 1209244
 
5.6%
Other values (11) 4232367
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21766405
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 2418488
11.1%
- 2418488
11.1%
a 2418488
11.1%
2 1813866
8.3%
4 1813866
8.3%
b 1813866
8.3%
5 1209244
 
5.6%
8 1209244
 
5.6%
3 1209244
 
5.6%
9 1209244
 
5.6%
Other values (11) 4232367
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21766405
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 2418488
11.1%
- 2418488
11.1%
a 2418488
11.1%
2 1813866
8.3%
4 1813866
8.3%
b 1813866
8.3%
5 1209244
 
5.6%
8 1209244
 
5.6%
3 1209244
 
5.6%
9 1209244
 
5.6%
Other values (11) 4232367
19.4%
Distinct2
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:41:58.430575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length2
Mean length2.000014885
Min length2

Characters and Unicode

Total characters1209255
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 604622
> 99.9%
san 1
 
< 0.1%
antonio 1
 
< 0.1%
2025-01-07T10:41:58.528048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 604623
50.0%
U 604622
50.0%
n 3
 
< 0.1%
o 2
 
< 0.1%
a 1
 
< 0.1%
1
 
< 0.1%
A 1
 
< 0.1%
t 1
 
< 0.1%
i 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1209255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 604623
50.0%
U 604622
50.0%
n 3
 
< 0.1%
o 2
 
< 0.1%
a 1
 
< 0.1%
1
 
< 0.1%
A 1
 
< 0.1%
t 1
 
< 0.1%
i 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1209255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 604623
50.0%
U 604622
50.0%
n 3
 
< 0.1%
o 2
 
< 0.1%
a 1
 
< 0.1%
1
 
< 0.1%
A 1
 
< 0.1%
t 1
 
< 0.1%
i 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1209255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 604623
50.0%
U 604622
50.0%
n 3
 
< 0.1%
o 2
 
< 0.1%
a 1
 
< 0.1%
1
 
< 0.1%
A 1
 
< 0.1%
t 1
 
< 0.1%
i 1
 
< 0.1%
Distinct186893
Distinct (%)30.9%
Missing2
Missing (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:41:58.674969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.9957792
Min length2

Characters and Unicode

Total characters14508424
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38990 ?
Unique (%)6.4%

Sample

1st row2024-12-02T13:57:44.315Z
2nd row2024-12-02T13:57:18.321Z
3rd row2024-12-02T13:59:05.381Z
4th row2024-12-02T13:57:22.450Z
5th row2024-12-02T13:57:21.275Z
ValueCountFrequency (%)
2024-12-02t13:57:53.908z 16
 
< 0.1%
2024-12-02t13:57:26.378z 16
 
< 0.1%
2024-12-02t13:57:45.539z 16
 
< 0.1%
2024-12-02t13:57:59.931z 16
 
< 0.1%
2024-12-02t13:57:23.279z 15
 
< 0.1%
2024-12-02t13:58:53.448z 15
 
< 0.1%
2024-12-02t13:57:28.641z 15
 
< 0.1%
2024-12-02t13:57:47.898z 15
 
< 0.1%
2024-12-02t13:56:41.760z 15
 
< 0.1%
2024-12-02t13:57:51.108z 15
 
< 0.1%
Other values (186883) 604470
> 99.9%
2025-01-07T10:41:58.881714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2760432
19.0%
0 1532584
10.6%
1 1525143
10.5%
- 1209244
8.3%
: 1209244
8.3%
4 972748
 
6.7%
5 960823
 
6.6%
3 957684
 
6.6%
T 604622
 
4.2%
Z 604622
 
4.2%
Other values (7) 2171278
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14508424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2760432
19.0%
0 1532584
10.6%
1 1525143
10.5%
- 1209244
8.3%
: 1209244
8.3%
4 972748
 
6.7%
5 960823
 
6.6%
3 957684
 
6.6%
T 604622
 
4.2%
Z 604622
 
4.2%
Other values (7) 2171278
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14508424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2760432
19.0%
0 1532584
10.6%
1 1525143
10.5%
- 1209244
8.3%
: 1209244
8.3%
4 972748
 
6.7%
5 960823
 
6.6%
3 957684
 
6.6%
T 604622
 
4.2%
Z 604622
 
4.2%
Other values (7) 2171278
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14508424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2760432
19.0%
0 1532584
10.6%
1 1525143
10.5%
- 1209244
8.3%
: 1209244
8.3%
4 972748
 
6.7%
5 960823
 
6.6%
3 957684
 
6.6%
T 604622
 
4.2%
Z 604622
 
4.2%
Other values (7) 2171278
15.0%

elevation
Real number (ℝ)

Missing 

Distinct1990
Distinct (%)4.3%
Missing557870
Missing (%)92.3%
Infinite0
Infinite (%)0.0%
Mean1218.67681
Minimum-195
Maximum12100
Zeros129
Zeros (%)< 0.1%
Negative21
Negative (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:41:58.958219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-195
5-th percentile39.75
Q1316
median1021
Q31830
95-th percentile3181
Maximum12100
Range12295
Interquartile range (IQR)1514

Descriptive statistics

Standard deviation1024.256224
Coefficient of variation (CV)0.8404658364
Kurtosis0.9428299526
Mean1218.67681
Median Absolute Deviation (MAD)734
Skewness0.9271847018
Sum56980452.91
Variance1049100.813
MonotonicityNot monotonic
2025-01-07T10:41:59.122482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2743 1163
 
0.2%
3353 875
 
0.1%
1524 704
 
0.1%
1829 659
 
0.1%
1100 556
 
0.1%
427 524
 
0.1%
914 524
 
0.1%
250 506
 
0.1%
200 496
 
0.1%
1372 495
 
0.1%
Other values (1980) 40254
 
6.7%
(Missing) 557870
92.3%
ValueCountFrequency (%)
-195 3
 
< 0.1%
-122 4
 
< 0.1%
-110 1
 
< 0.1%
-9 12
< 0.1%
-4.5 1
 
< 0.1%
ValueCountFrequency (%)
12100 2
< 0.1%
9550 1
< 0.1%
8230 1
< 0.1%
8000 1
< 0.1%
7650 1
< 0.1%

elevationAccuracy
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct215
Distinct (%)0.7%
Missing573282
Missing (%)94.8%
Infinite0
Infinite (%)0.0%
Mean322.7892739
Minimum0
Maximum7804668
Zeros27237
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:59.188481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile152.5
Maximum7804668
Range7804668
Interquartile range (IQR)0

Descriptive statistics

Standard deviation44752.27397
Coefficient of variation (CV)138.6423825
Kurtosis29538.56492
Mean322.7892739
Median Absolute Deviation (MAD)0
Skewness170.1239669
Sum10117507
Variance2002766026
MonotonicityNot monotonic
2025-01-07T10:41:59.258553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27237
 
4.5%
152.5 408
 
0.1%
30.5 325
 
0.1%
457 257
 
< 0.1%
100 249
 
< 0.1%
15 217
 
< 0.1%
914 185
 
< 0.1%
50 181
 
< 0.1%
305 175
 
< 0.1%
25 147
 
< 0.1%
Other values (205) 1963
 
0.3%
(Missing) 573282
94.8%
ValueCountFrequency (%)
0 27237
4.5%
0.5 1
 
< 0.1%
1.25 7
 
< 0.1%
1.5 1
 
< 0.1%
1.75 5
 
< 0.1%
ValueCountFrequency (%)
7804668 1
< 0.1%
1364691 1
< 0.1%
9900 2
< 0.1%
7620 1
< 0.1%
1980 1
< 0.1%

depth
Real number (ℝ)

Missing 

Distinct12
Distinct (%)35.3%
Missing604592
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean769.5882353
Minimum110
Maximum7600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:59.313144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile110
Q1250
median364.5
Q3880
95-th percentile1719.6
Maximum7600
Range7490
Interquartile range (IQR)630

Descriptive statistics

Standard deviation1300.478143
Coefficient of variation (CV)1.689836309
Kurtosis24.42733058
Mean769.5882353
Median Absolute Deviation (MAD)254.5
Skewness4.657046122
Sum26166
Variance1691243.401
MonotonicityNot monotonic
2025-01-07T10:41:59.360647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
250 9
 
< 0.1%
110 6
 
< 0.1%
880 6
 
< 0.1%
370 3
 
< 0.1%
775 2
 
< 0.1%
1707 2
 
< 0.1%
359 1
 
< 0.1%
1400 1
 
< 0.1%
1743 1
 
< 0.1%
500 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 604592
> 99.9%
ValueCountFrequency (%)
110 6
< 0.1%
250 9
< 0.1%
300 1
 
< 0.1%
359 1
 
< 0.1%
370 3
 
< 0.1%
ValueCountFrequency (%)
7600 1
 
< 0.1%
1743 1
 
< 0.1%
1707 2
 
< 0.1%
1400 1
 
< 0.1%
880 6
< 0.1%

depthAccuracy
Real number (ℝ)

Missing 

Distinct2
Distinct (%)18.2%
Missing604615
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean60
Minimum0
Maximum110
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:59.406465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median110
Q3110
95-th percentile110
Maximum110
Range110
Interquartile range (IQR)110

Descriptive statistics

Standard deviation57.44562647
Coefficient of variation (CV)0.9574271078
Kurtosis-2.444444444
Mean60
Median Absolute Deviation (MAD)0
Skewness-0.2127615795
Sum660
Variance3300
MonotonicityNot monotonic
2025-01-07T10:41:59.449972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
110 6
 
< 0.1%
0 5
 
< 0.1%
(Missing) 604615
> 99.9%
ValueCountFrequency (%)
0 5
< 0.1%
110 6
< 0.1%
ValueCountFrequency (%)
110 6
< 0.1%
0 5
< 0.1%

distanceFromCentroidInMeters
Real number (ℝ)

Missing 

Distinct259
Distinct (%)8.6%
Missing601631
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean2140.980122
Minimum0
Maximum4970.343235
Zeros634
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:41:59.506747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1347.4636295
median1970.112454
Q34105.643933
95-th percentile4301.19944
Maximum4970.343235
Range4970.343235
Interquartile range (IQR)3758.180303

Descriptive statistics

Standard deviation1780.465223
Coefficient of variation (CV)0.8316122156
Kurtosis-1.717128587
Mean2140.980122
Median Absolute Deviation (MAD)1970.112454
Skewness0.06179569977
Sum6412235.465
Variance3170056.409
MonotonicityNot monotonic
2025-01-07T10:41:59.572984image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 634
 
0.1%
4105.643933 593
 
0.1%
949.7490617 164
 
< 0.1%
513.8699121 112
 
< 0.1%
4282.192004 80
 
< 0.1%
347.4636295 75
 
< 0.1%
1404.207532 56
 
< 0.1%
512.15841 45
 
< 0.1%
247.4780297 40
 
< 0.1%
3590.235565 39
 
< 0.1%
Other values (249) 1157
 
0.2%
(Missing) 601631
99.5%
ValueCountFrequency (%)
0 634
0.1%
2.43498748 2
 
< 0.1%
3.654429861 1
 
< 0.1%
67.25063149 1
 
< 0.1%
188.4584836 15
 
< 0.1%
ValueCountFrequency (%)
4970.343235 1
< 0.1%
4965.486359 1
< 0.1%
4959.773741 2
< 0.1%
4942.882538 1
< 0.1%
4933.433353 1
< 0.1%

issue
Text

Distinct143
Distinct (%)< 0.1%
Missing2735
Missing (%)0.5%
Memory size4.6 MiB
2025-01-07T10:41:59.636600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length200
Median length198
Mean length91.49480222
Min length15

Characters and Unicode

Total characters55069898
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)< 0.1%

Sample

1st rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
2nd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
3rd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
4th rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED_FROM_COORDINATES
5th rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED_FROM_COORDINATES
ValueCountFrequency (%)
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_derived_from_coordinates 248827
41.3%
occurrence_status_inferred_from_individual_count 146633
24.4%
occurrence_status_inferred_from_individual_count;continent_derived_from_country 70820
 
11.8%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_derived_from_coordinates;taxon_match_higherrank 32084
 
5.3%
occurrence_status_inferred_from_individual_count;taxon_match_higherrank 31693
 
5.3%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;geodetic_datum_invalid;continent_derived_from_coordinates 21011
 
3.5%
occurrence_status_inferred_from_individual_count;continent_derived_from_country;taxon_match_higherrank 11828
 
2.0%
occurrence_status_inferred_from_individual_count;taxon_match_fuzzy 6987
 
1.2%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_derived_from_coordinates;taxon_match_fuzzy 6167
 
1.0%
occurrence_status_inferred_from_individual_count;country_invalid 5364
 
0.9%
Other values (133) 20477
 
3.4%
2025-01-07T10:41:59.777259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 5457778
9.9%
E 5050863
 
9.2%
R 4409788
 
8.0%
N 4266550
 
7.7%
I 4035031
 
7.3%
D 3989481
 
7.2%
T 3935075
 
7.1%
O 3811449
 
6.9%
C 3682307
 
6.7%
U 3181760
 
5.8%
Other values (18) 13249816
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55069898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 5457778
9.9%
E 5050863
 
9.2%
R 4409788
 
8.0%
N 4266550
 
7.7%
I 4035031
 
7.3%
D 3989481
 
7.2%
T 3935075
 
7.1%
O 3811449
 
6.9%
C 3682307
 
6.7%
U 3181760
 
5.8%
Other values (18) 13249816
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55069898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 5457778
9.9%
E 5050863
 
9.2%
R 4409788
 
8.0%
N 4266550
 
7.7%
I 4035031
 
7.3%
D 3989481
 
7.2%
T 3935075
 
7.1%
O 3811449
 
6.9%
C 3682307
 
6.7%
U 3181760
 
5.8%
Other values (18) 13249816
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55069898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 5457778
9.9%
E 5050863
 
9.2%
R 4409788
 
8.0%
N 4266550
 
7.7%
I 4035031
 
7.3%
D 3989481
 
7.2%
T 3935075
 
7.1%
O 3811449
 
6.9%
C 3682307
 
6.7%
U 3181760
 
5.8%
Other values (18) 13249816
24.1%

mediaType
Text

Missing 

Distinct19
Distinct (%)< 0.1%
Missing369838
Missing (%)61.2%
Memory size4.6 MiB
2025-01-07T10:41:59.833818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length241
Median length10
Mean length15.46893794
Min length10

Characters and Unicode

Total characters3631921
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowStillImage
2nd rowStillImage
3rd rowStillImage
4th rowStillImage
5th rowStillImage
ValueCountFrequency (%)
stillimage 192418
82.0%
stillimage;stillimage;stillimage;stillimage 14664
 
6.2%
stillimage;stillimage;stillimage 10110
 
4.3%
stillimage;stillimage 8407
 
3.6%
stillimage;stillimage;stillimage;stillimage;stillimage 5480
 
2.3%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 1551
 
0.7%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 1253
 
0.5%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 626
 
0.3%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 139
 
0.1%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 71
 
< 0.1%
Other values (9) 69
 
< 0.1%
2025-01-07T10:41:59.947624image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 703038
19.4%
S 351519
9.7%
t 351519
9.7%
i 351519
9.7%
I 351519
9.7%
m 351519
9.7%
a 351519
9.7%
g 351519
9.7%
e 351519
9.7%
; 116731
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3631921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 703038
19.4%
S 351519
9.7%
t 351519
9.7%
i 351519
9.7%
I 351519
9.7%
m 351519
9.7%
a 351519
9.7%
g 351519
9.7%
e 351519
9.7%
; 116731
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3631921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 703038
19.4%
S 351519
9.7%
t 351519
9.7%
i 351519
9.7%
I 351519
9.7%
m 351519
9.7%
a 351519
9.7%
g 351519
9.7%
e 351519
9.7%
; 116731
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3631921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 703038
19.4%
S 351519
9.7%
t 351519
9.7%
i 351519
9.7%
I 351519
9.7%
m 351519
9.7%
a 351519
9.7%
g 351519
9.7%
e 351519
9.7%
; 116731
 
3.2%

hasCoordinate
Unsupported

Rejected  Unsupported 

Missing2
Missing (%)< 0.1%
Memory size4.6 MiB

hasGeospatialIssues
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size4.6 MiB
False
603891 
True
 
731
(Missing)
 
4
ValueCountFrequency (%)
False 603891
99.9%
True 731
 
0.1%
(Missing) 4
 
< 0.1%
2025-01-07T10:42:00.001971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

taxonKey
Real number (ℝ)

Distinct203336
Distinct (%)33.6%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3294378.297
Minimum0
Maximum12386548
Zeros4644
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:00.057482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1047401
Q11366107.25
median1730257
Q35051399
95-th percentile9249151.55
Maximum12386548
Range12386548
Interquartile range (IQR)3685291.75

Descriptive statistics

Standard deviation2875765.445
Coefficient of variation (CV)0.8729311529
Kurtosis0.5073414626
Mean3294378.297
Median Absolute Deviation (MAD)389933
Skewness1.270519999
Sum1.991853595 × 1012
Variance8.270026892 × 1012
MonotonicityNot monotonic
2025-01-07T10:42:00.123602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1340278 10672
 
1.8%
1340525 6264
 
1.0%
0 4644
 
0.8%
1340393 4071
 
0.7%
10976534 3621
 
0.6%
789 3466
 
0.6%
1340467 3340
 
0.6%
9164 3176
 
0.5%
1340350 3129
 
0.5%
1341979 2431
 
0.4%
Other values (203326) 559808
92.6%
ValueCountFrequency (%)
0 4644
0.8%
1 18
 
< 0.1%
54 6
 
< 0.1%
216 180
 
< 0.1%
360 11
 
< 0.1%
ValueCountFrequency (%)
12386548 3
< 0.1%
12385522 3
< 0.1%
12384935 1
 
< 0.1%
12384655 4
< 0.1%
12384435 2
< 0.1%

acceptedTaxonKey
Real number (ℝ)

Distinct188378
Distinct (%)31.4%
Missing4648
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3147779.704
Minimum1
Maximum12386548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:00.188778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1048343
Q11358413.75
median1660742
Q35038798.75
95-th percentile9429837.45
Maximum12386548
Range12386547
Interquartile range (IQR)3680385

Descriptive statistics

Standard deviation2782156.055
Coefficient of variation (CV)0.8838471292
Kurtosis0.9801945181
Mean3147779.704
Median Absolute Deviation (MAD)320464
Skewness1.423983392
Sum1.888598571 × 1012
Variance7.740392314 × 1012
MonotonicityNot monotonic
2025-01-07T10:42:00.253800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1340278 10672
 
1.8%
1340525 6265
 
1.0%
1340393 4073
 
0.7%
10409744 3623
 
0.6%
789 3466
 
0.6%
1340467 3343
 
0.6%
9164 3176
 
0.5%
1340350 3129
 
0.5%
1341979 2431
 
0.4%
1340485 2119
 
0.4%
Other values (188368) 557681
92.2%
(Missing) 4648
 
0.8%
ValueCountFrequency (%)
1 18
 
< 0.1%
54 6
 
< 0.1%
216 180
< 0.1%
360 11
 
< 0.1%
361 30
 
< 0.1%
ValueCountFrequency (%)
12386548 3
< 0.1%
12371456 3
< 0.1%
12370373 1
 
< 0.1%
12363953 5
< 0.1%
12356386 2
 
< 0.1%

kingdomKey
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.992319168
Minimum0
Maximum1
Zeros4644
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:00.309203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08730320393
Coefficient of variation (CV)0.08797895551
Kurtosis125.2030147
Mean0.992319168
Median Absolute Deviation (MAD)0
Skewness-11.27841303
Sum599978
Variance0.007621849417
MonotonicityNot monotonic
2025-01-07T10:42:00.359709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 599978
99.2%
0 4644
 
0.8%
(Missing) 4
 
< 0.1%
ValueCountFrequency (%)
0 4644
 
0.8%
1 599978
99.2%
ValueCountFrequency (%)
1 599978
99.2%
0 4644
 
0.8%

phylumKey
Real number (ℝ)

Skewed 

Distinct7
Distinct (%)< 0.1%
Missing5247
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean53.99970803
Minimum43
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:00.407140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile54
Q154
median54
Q354
95-th percentile54
Maximum63
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06930532102
Coefficient of variation (CV)0.00128343881
Kurtosis22775.60419
Mean53.99970803
Median Absolute Deviation (MAD)0
Skewness-111.556886
Sum32366291
Variance0.004803227522
MonotonicityNot monotonic
2025-01-07T10:42:00.458645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
54 599346
99.1%
43 18
 
< 0.1%
62 6
 
< 0.1%
52 5
 
< 0.1%
44 2
 
< 0.1%
63 1
 
< 0.1%
50 1
 
< 0.1%
(Missing) 5247
 
0.9%
ValueCountFrequency (%)
43 18
 
< 0.1%
44 2
 
< 0.1%
50 1
 
< 0.1%
52 5
 
< 0.1%
54 599346
99.1%
ValueCountFrequency (%)
63 1
 
< 0.1%
62 6
 
< 0.1%
54 599346
99.1%
52 5
 
< 0.1%
50 1
 
< 0.1%

classKey
Real number (ℝ)

Skewed 

Distinct13
Distinct (%)< 0.1%
Missing5283
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean17617.67435
Minimum121
Maximum11377931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:00.511724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile216
Q1216
median216
Q3216
95-th percentile216
Maximum11377931
Range11377810
Interquartile range (IQR)0

Descriptive statistics

Standard deviation433058.3594
Coefficient of variation (CV)24.58090386
Kurtosis617.22568
Mean17617.67435
Median Absolute Deviation (MAD)0
Skewness24.87357061
Sum1.05590298 × 1010
Variance1.875395426 × 1011
MonotonicityNot monotonic
2025-01-07T10:42:00.650346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
216 588111
97.3%
367 7917
 
1.3%
361 1599
 
0.3%
10713444 820
 
0.1%
360 736
 
0.1%
11374670 77
 
< 0.1%
11377931 62
 
< 0.1%
7742773 8
 
< 0.1%
229 5
 
< 0.1%
143 4
 
< 0.1%
Other values (3) 4
 
< 0.1%
(Missing) 5283
 
0.9%
ValueCountFrequency (%)
121 1
 
< 0.1%
143 4
 
< 0.1%
216 588111
97.3%
221 1
 
< 0.1%
225 2
 
< 0.1%
ValueCountFrequency (%)
11377931 62
 
< 0.1%
11374670 77
 
< 0.1%
10713444 820
 
0.1%
7742773 8
 
< 0.1%
367 7917
1.3%

orderKey
Unsupported

Rejected  Unsupported 

Missing5577
Missing (%)0.9%
Memory size4.6 MiB

familyKey
Unsupported

Missing  Rejected  Unsupported 

Missing11642
Missing (%)1.9%
Memory size4.6 MiB

genusKey
Unsupported

Missing  Rejected  Unsupported 

Missing19883
Missing (%)3.3%
Memory size4.6 MiB

subgenusKey
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:00.689218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters14
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInsecta
2nd rowInsecta
ValueCountFrequency (%)
insecta 2
100.0%
2025-01-07T10:42:00.780725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 2
14.3%
n 2
14.3%
s 2
14.3%
e 2
14.3%
c 2
14.3%
t 2
14.3%
a 2
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 2
14.3%
n 2
14.3%
s 2
14.3%
e 2
14.3%
c 2
14.3%
t 2
14.3%
a 2
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 2
14.3%
n 2
14.3%
s 2
14.3%
e 2
14.3%
c 2
14.3%
t 2
14.3%
a 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 2
14.3%
n 2
14.3%
s 2
14.3%
e 2
14.3%
c 2
14.3%
t 2
14.3%
a 2
14.3%

speciesKey
Unsupported

Missing  Rejected  Unsupported 

Missing109501
Missing (%)18.1%
Memory size4.6 MiB

species
Text

Missing 

Distinct168987
Distinct (%)34.1%
Missing109503
Missing (%)18.1%
Memory size4.6 MiB
2025-01-07T10:42:01.006131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length32
Mean length18.63352945
Min length6

Characters and Unicode

Total characters9225889
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121697 ?
Unique (%)24.6%

Sample

1st rowCamponotus rufoglaucus
2nd rowAthrips mesoleuca
3rd rowParanthrene asilipennis
4th rowAcanthagrion trilobatum
5th rowCalathus ingratus
ValueCountFrequency (%)
bombus 51714
 
5.2%
xylocopa 9795
 
1.0%
argia 8430
 
0.9%
enallagma 7850
 
0.8%
crambus 7738
 
0.8%
ischnura 7433
 
0.8%
sylvicola 6290
 
0.6%
sympetrum 5960
 
0.6%
apis 4956
 
0.5%
lestes 4143
 
0.4%
Other values (101139) 875937
88.5%
2025-01-07T10:42:01.309581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1016887
 
11.0%
i 815089
 
8.8%
s 718014
 
7.8%
e 632080
 
6.9%
o 593441
 
6.4%
r 547183
 
5.9%
l 514243
 
5.6%
495123
 
5.4%
u 469909
 
5.1%
n 449274
 
4.9%
Other values (44) 2974646
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9225889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1016887
 
11.0%
i 815089
 
8.8%
s 718014
 
7.8%
e 632080
 
6.9%
o 593441
 
6.4%
r 547183
 
5.9%
l 514243
 
5.6%
495123
 
5.4%
u 469909
 
5.1%
n 449274
 
4.9%
Other values (44) 2974646
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9225889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1016887
 
11.0%
i 815089
 
8.8%
s 718014
 
7.8%
e 632080
 
6.9%
o 593441
 
6.4%
r 547183
 
5.9%
l 514243
 
5.6%
495123
 
5.4%
u 469909
 
5.1%
n 449274
 
4.9%
Other values (44) 2974646
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9225889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1016887
 
11.0%
i 815089
 
8.8%
s 718014
 
7.8%
e 632080
 
6.9%
o 593441
 
6.4%
r 547183
 
5.9%
l 514243
 
5.6%
495123
 
5.4%
u 469909
 
5.1%
n 449274
 
4.9%
Other values (44) 2974646
32.2%
Distinct188378
Distinct (%)31.4%
Missing4646
Missing (%)0.8%
Memory size4.6 MiB
2025-01-07T10:42:01.555217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length239
Median length106
Mean length31.58040101
Min length5

Characters and Unicode

Total characters18947609
Distinct characters108
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134599 ?
Unique (%)22.4%

Sample

1st rowCamponotus rufoglaucus var. rufigenis Forel
2nd rowAthrips mesoleuca Lower, 1900
3rd rowParanthrene asilipennis (Boisduval, 1832)
4th rowAcanthagrion trilobatum Leonard, 1977
5th rowCalathus ingratus Dejean, 1828
ValueCountFrequency (%)
bombus 62386
 
2.7%
28889
 
1.2%
hagen 24360
 
1.0%
cresson 24243
 
1.0%
1861 18841
 
0.8%
fabricius 17279
 
0.7%
1863 16815
 
0.7%
selys 16399
 
0.7%
say 15686
 
0.7%
latreille 15381
 
0.7%
Other values (114566) 2087838
89.7%
2025-01-07T10:42:01.866594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1728137
 
9.1%
a 1474905
 
7.8%
e 1198055
 
6.3%
i 1144904
 
6.0%
s 1048999
 
5.5%
r 967679
 
5.1%
o 892139
 
4.7%
l 791989
 
4.2%
n 763156
 
4.0%
1 665031
 
3.5%
Other values (98) 8272615
43.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18947609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1728137
 
9.1%
a 1474905
 
7.8%
e 1198055
 
6.3%
i 1144904
 
6.0%
s 1048999
 
5.5%
r 967679
 
5.1%
o 892139
 
4.7%
l 791989
 
4.2%
n 763156
 
4.0%
1 665031
 
3.5%
Other values (98) 8272615
43.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18947609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1728137
 
9.1%
a 1474905
 
7.8%
e 1198055
 
6.3%
i 1144904
 
6.0%
s 1048999
 
5.5%
r 967679
 
5.1%
o 892139
 
4.7%
l 791989
 
4.2%
n 763156
 
4.0%
1 665031
 
3.5%
Other values (98) 8272615
43.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18947609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1728137
 
9.1%
a 1474905
 
7.8%
e 1198055
 
6.3%
i 1144904
 
6.0%
s 1048999
 
5.5%
r 967679
 
5.1%
o 892139
 
4.7%
l 791989
 
4.2%
n 763156
 
4.0%
1 665031
 
3.5%
Other values (98) 8272615
43.7%
Distinct245043
Distinct (%)40.8%
Missing4630
Missing (%)0.8%
Memory size4.6 MiB
2025-01-07T10:42:02.115691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length68
Median length61
Mean length20.7704068
Min length3

Characters and Unicode

Total characters12462161
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201366 ?
Unique (%)33.6%

Sample

1st rowCamponotus (Myrmosericus) rufoglaucus cinctella var. rufigenis
2nd rowAthrips mesoleuca
3rd rowParanthrene asilipennis
4th rowAcanthagrion trilobatum
5th rowCalathus nanulus
ValueCountFrequency (%)
bombus 69588
 
5.3%
sp 44392
 
3.4%
pyrobombus 21248
 
1.6%
xylocopa 12219
 
0.9%
unidentified 9028
 
0.7%
argia 8663
 
0.7%
apis 8601
 
0.6%
enallagma 7977
 
0.6%
crambus 7970
 
0.6%
ischnura 7456
 
0.6%
Other values (130808) 1127237
85.1%
2025-01-07T10:42:02.439265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1253913
 
10.1%
i 1043196
 
8.4%
s 971230
 
7.8%
o 842744
 
6.8%
e 820779
 
6.6%
724383
 
5.8%
r 712701
 
5.7%
l 623014
 
5.0%
u 614900
 
4.9%
n 589792
 
4.7%
Other values (72) 4265509
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12462161
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1253913
 
10.1%
i 1043196
 
8.4%
s 971230
 
7.8%
o 842744
 
6.8%
e 820779
 
6.6%
724383
 
5.8%
r 712701
 
5.7%
l 623014
 
5.0%
u 614900
 
4.9%
n 589792
 
4.7%
Other values (72) 4265509
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12462161
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1253913
 
10.1%
i 1043196
 
8.4%
s 971230
 
7.8%
o 842744
 
6.8%
e 820779
 
6.6%
724383
 
5.8%
r 712701
 
5.7%
l 623014
 
5.0%
u 614900
 
4.9%
n 589792
 
4.7%
Other values (72) 4265509
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12462161
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1253913
 
10.1%
i 1043196
 
8.4%
s 971230
 
7.8%
o 842744
 
6.8%
e 820779
 
6.6%
724383
 
5.8%
r 712701
 
5.7%
l 623014
 
5.0%
u 614900
 
4.9%
n 589792
 
4.7%
Other values (72) 4265509
34.2%

typifiedName
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

protocol
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:42:02.493432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1813866
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEML
2nd rowEML
3rd rowEML
4th rowEML
5th rowEML
ValueCountFrequency (%)
eml 604622
100.0%
2025-01-07T10:42:02.585280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 604622
33.3%
M 604622
33.3%
L 604622
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1813866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 604622
33.3%
M 604622
33.3%
L 604622
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1813866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 604622
33.3%
M 604622
33.3%
L 604622
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1813866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 604622
33.3%
M 604622
33.3%
L 604622
33.3%
Distinct186894
Distinct (%)30.9%
Missing2
Missing (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:42:02.733953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.9958007
Min length7

Characters and Unicode

Total characters14508437
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38992 ?
Unique (%)6.4%

Sample

1st row2024-12-02T13:57:44.315Z
2nd row2024-12-02T13:57:18.321Z
3rd row2024-12-02T13:59:05.381Z
4th row2024-12-02T13:57:22.450Z
5th row2024-12-02T13:57:21.275Z
ValueCountFrequency (%)
2024-12-02t13:57:26.378z 16
 
< 0.1%
2024-12-02t13:57:59.931z 16
 
< 0.1%
2024-12-02t13:57:53.908z 16
 
< 0.1%
2024-12-02t13:57:45.539z 16
 
< 0.1%
2024-12-02t13:57:23.279z 15
 
< 0.1%
2024-12-02t13:57:51.108z 15
 
< 0.1%
2024-12-02t13:57:47.898z 15
 
< 0.1%
2024-12-02t13:56:41.760z 15
 
< 0.1%
2024-12-02t13:56:43.735z 15
 
< 0.1%
2024-12-02t13:58:53.448z 15
 
< 0.1%
Other values (186884) 604470
> 99.9%
2025-01-07T10:42:02.948045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2760432
19.0%
0 1532584
10.6%
1 1525143
10.5%
- 1209244
8.3%
: 1209244
8.3%
4 972748
 
6.7%
5 960823
 
6.6%
3 957684
 
6.6%
T 604623
 
4.2%
Z 604622
 
4.2%
Other values (19) 2171290
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14508437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2760432
19.0%
0 1532584
10.6%
1 1525143
10.5%
- 1209244
8.3%
: 1209244
8.3%
4 972748
 
6.7%
5 960823
 
6.6%
3 957684
 
6.6%
T 604623
 
4.2%
Z 604622
 
4.2%
Other values (19) 2171290
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14508437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2760432
19.0%
0 1532584
10.6%
1 1525143
10.5%
- 1209244
8.3%
: 1209244
8.3%
4 972748
 
6.7%
5 960823
 
6.6%
3 957684
 
6.6%
T 604623
 
4.2%
Z 604622
 
4.2%
Other values (19) 2171290
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14508437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2760432
19.0%
0 1532584
10.6%
1 1525143
10.5%
- 1209244
8.3%
: 1209244
8.3%
4 972748
 
6.7%
5 960823
 
6.6%
3 957684
 
6.6%
T 604623
 
4.2%
Z 604622
 
4.2%
Other values (19) 2171290
15.0%
Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:42:03.009327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.99994873
Min length7

Characters and Unicode

Total characters14510945
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2024-12-02T11:48:23.416Z
2nd row2024-12-02T11:48:23.416Z
3rd row2024-12-02T11:48:23.416Z
4th row2024-12-02T11:48:23.416Z
5th row2024-12-02T11:48:23.416Z
ValueCountFrequency (%)
2024-12-02t11:48:23.416z 604622
> 99.9%
trogoderma 1
 
< 0.1%
aphytis 1
 
< 0.1%
2025-01-07T10:42:03.117224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3023110
20.8%
1 2418488
16.7%
4 1813866
12.5%
0 1209244
 
8.3%
- 1209244
 
8.3%
: 1209244
 
8.3%
T 604623
 
4.2%
8 604622
 
4.2%
3 604622
 
4.2%
. 604622
 
4.2%
Other values (16) 1209260
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14510945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3023110
20.8%
1 2418488
16.7%
4 1813866
12.5%
0 1209244
 
8.3%
- 1209244
 
8.3%
: 1209244
 
8.3%
T 604623
 
4.2%
8 604622
 
4.2%
3 604622
 
4.2%
. 604622
 
4.2%
Other values (16) 1209260
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14510945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3023110
20.8%
1 2418488
16.7%
4 1813866
12.5%
0 1209244
 
8.3%
- 1209244
 
8.3%
: 1209244
 
8.3%
T 604623
 
4.2%
8 604622
 
4.2%
3 604622
 
4.2%
. 604622
 
4.2%
Other values (16) 1209260
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14510945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3023110
20.8%
1 2418488
16.7%
4 1813866
12.5%
0 1209244
 
8.3%
- 1209244
 
8.3%
: 1209244
 
8.3%
T 604623
 
4.2%
8 604622
 
4.2%
3 604622
 
4.2%
. 604622
 
4.2%
Other values (16) 1209260
8.3%

repatriated
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing162658
Missing (%)26.9%
Memory size4.6 MiB
True
224080 
False
217888 
(Missing)
162658 
ValueCountFrequency (%)
True 224080
37.1%
False 217888
36.0%
(Missing) 162658
26.9%
2025-01-07T10:42:03.174732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

relativeOrganismQuantity
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

projectId
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:03.205223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowroseni
ValueCountFrequency (%)
roseni 1
100.0%
2025-01-07T10:42:03.297196image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 1
16.7%
o 1
16.7%
s 1
16.7%
e 1
16.7%
n 1
16.7%
i 1
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1
16.7%
o 1
16.7%
s 1
16.7%
e 1
16.7%
n 1
16.7%
i 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1
16.7%
o 1
16.7%
s 1
16.7%
e 1
16.7%
n 1
16.7%
i 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1
16.7%
o 1
16.7%
s 1
16.7%
e 1
16.7%
n 1
16.7%
i 1
16.7%

isSequenced
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size4.6 MiB
False
604622 
(Missing)
 
4
ValueCountFrequency (%)
False 604622
> 99.9%
(Missing) 4
 
< 0.1%
2025-01-07T10:42:03.350708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

gbifRegion
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing163113
Missing (%)27.0%
Memory size4.6 MiB
2025-01-07T10:42:03.387559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length11.14388478
Min length4

Characters and Unicode

Total characters4920170
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowLATIN_AMERICA
3rd rowNORTH_AMERICA
4th rowNORTH_AMERICA
5th rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 234151
53.0%
latin_america 104373
23.6%
asia 55886
 
12.7%
africa 22020
 
5.0%
oceania 13164
 
3.0%
europe 11911
 
2.7%
antarctica 8
 
< 0.1%
2025-01-07T10:42:03.496684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 963585
19.6%
R 606614
12.3%
I 533975
10.9%
E 375510
 
7.6%
C 373724
 
7.6%
N 351696
 
7.1%
T 338540
 
6.9%
M 338524
 
6.9%
_ 338524
 
6.9%
O 259226
 
5.3%
Other values (6) 440252
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 963585
19.6%
R 606614
12.3%
I 533975
10.9%
E 375510
 
7.6%
C 373724
 
7.6%
N 351696
 
7.1%
T 338540
 
6.9%
M 338524
 
6.9%
_ 338524
 
6.9%
O 259226
 
5.3%
Other values (6) 440252
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 963585
19.6%
R 606614
12.3%
I 533975
10.9%
E 375510
 
7.6%
C 373724
 
7.6%
N 351696
 
7.1%
T 338540
 
6.9%
M 338524
 
6.9%
_ 338524
 
6.9%
O 259226
 
5.3%
Other values (6) 440252
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 963585
19.6%
R 606614
12.3%
I 533975
10.9%
E 375510
 
7.6%
C 373724
 
7.6%
N 351696
 
7.1%
T 338540
 
6.9%
M 338524
 
6.9%
_ 338524
 
6.9%
O 259226
 
5.3%
Other values (6) 440252
8.9%
Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size4.6 MiB
2025-01-07T10:42:03.547195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.99997685
Min length5

Characters and Unicode

Total characters7860098
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowNORTH_AMERICA
4th rowNORTH_AMERICA
5th rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 604622
> 99.9%
genus 1
 
< 0.1%
species 1
 
< 0.1%
2025-01-07T10:42:03.657814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 1209244
15.4%
A 1209244
15.4%
E 604625
7.7%
I 604623
7.7%
C 604623
7.7%
N 604623
7.7%
O 604622
7.7%
_ 604622
7.7%
H 604622
7.7%
T 604622
7.7%
Other values (5) 604628
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7860098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1209244
15.4%
A 1209244
15.4%
E 604625
7.7%
I 604623
7.7%
C 604623
7.7%
N 604623
7.7%
O 604622
7.7%
_ 604622
7.7%
H 604622
7.7%
T 604622
7.7%
Other values (5) 604628
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7860098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1209244
15.4%
A 1209244
15.4%
E 604625
7.7%
I 604623
7.7%
C 604623
7.7%
N 604623
7.7%
O 604622
7.7%
_ 604622
7.7%
H 604622
7.7%
T 604622
7.7%
Other values (5) 604628
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7860098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1209244
15.4%
A 1209244
15.4%
E 604625
7.7%
I 604623
7.7%
C 604623
7.7%
N 604623
7.7%
O 604622
7.7%
_ 604622
7.7%
H 604622
7.7%
T 604622
7.7%
Other values (5) 604628
7.7%

level0Gid
Text

Missing 

Distinct212
Distinct (%)0.1%
Missing288722
Missing (%)47.8%
Memory size4.6 MiB
2025-01-07T10:42:03.822490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters947712
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowCRI
2nd rowUSA
3rd rowUSA
4th rowDMA
5th rowCAN
ValueCountFrequency (%)
usa 196159
62.1%
can 14651
 
4.6%
mex 5495
 
1.7%
bra 4604
 
1.5%
cri 4530
 
1.4%
chl 4046
 
1.3%
zaf 3361
 
1.1%
ind 3261
 
1.0%
ken 3246
 
1.0%
arg 3226
 
1.0%
Other values (202) 73325
 
23.2%
2025-01-07T10:42:04.055725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 237901
25.1%
U 211103
22.3%
S 206824
21.8%
N 40079
 
4.2%
C 33198
 
3.5%
R 25245
 
2.7%
E 23068
 
2.4%
M 20525
 
2.2%
L 15881
 
1.7%
G 15551
 
1.6%
Other values (19) 118337
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 947712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 237901
25.1%
U 211103
22.3%
S 206824
21.8%
N 40079
 
4.2%
C 33198
 
3.5%
R 25245
 
2.7%
E 23068
 
2.4%
M 20525
 
2.2%
L 15881
 
1.7%
G 15551
 
1.6%
Other values (19) 118337
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 947712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 237901
25.1%
U 211103
22.3%
S 206824
21.8%
N 40079
 
4.2%
C 33198
 
3.5%
R 25245
 
2.7%
E 23068
 
2.4%
M 20525
 
2.2%
L 15881
 
1.7%
G 15551
 
1.6%
Other values (19) 118337
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 947712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 237901
25.1%
U 211103
22.3%
S 206824
21.8%
N 40079
 
4.2%
C 33198
 
3.5%
R 25245
 
2.7%
E 23068
 
2.4%
M 20525
 
2.2%
L 15881
 
1.7%
G 15551
 
1.6%
Other values (19) 118337
12.5%

level0Name
Text

Missing 

Distinct212
Distinct (%)0.1%
Missing288722
Missing (%)47.8%
Memory size4.6 MiB
2025-01-07T10:42:04.248546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length13
Mean length11.1129552
Min length4

Characters and Unicode

Total characters3510627
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowCosta Rica
2nd rowUnited States
3rd rowUnited States
4th rowDominica
5th rowCanada
ValueCountFrequency (%)
united 198236
36.6%
states 196177
36.2%
canada 14651
 
2.7%
méxico 5495
 
1.0%
brazil 4604
 
0.9%
costa 4530
 
0.8%
rica 4530
 
0.8%
chile 4046
 
0.7%
south 3768
 
0.7%
africa 3361
 
0.6%
Other values (247) 102079
18.9%
2025-01-07T10:42:04.505186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 614835
17.5%
e 449723
12.8%
a 371695
10.6%
n 279068
7.9%
i 278571
7.9%
d 238311
 
6.8%
225573
 
6.4%
s 217681
 
6.2%
S 208349
 
5.9%
U 199271
 
5.7%
Other values (52) 427550
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3510627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 614835
17.5%
e 449723
12.8%
a 371695
10.6%
n 279068
7.9%
i 278571
7.9%
d 238311
 
6.8%
225573
 
6.4%
s 217681
 
6.2%
S 208349
 
5.9%
U 199271
 
5.7%
Other values (52) 427550
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3510627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 614835
17.5%
e 449723
12.8%
a 371695
10.6%
n 279068
7.9%
i 278571
7.9%
d 238311
 
6.8%
225573
 
6.4%
s 217681
 
6.2%
S 208349
 
5.9%
U 199271
 
5.7%
Other values (52) 427550
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3510627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 614835
17.5%
e 449723
12.8%
a 371695
10.6%
n 279068
7.9%
i 278571
7.9%
d 238311
 
6.8%
225573
 
6.4%
s 217681
 
6.2%
S 208349
 
5.9%
U 199271
 
5.7%
Other values (52) 427550
12.2%

level1Gid
Text

Missing 

Distinct1995
Distinct (%)0.6%
Missing288806
Missing (%)47.8%
Memory size4.6 MiB
2025-01-07T10:42:04.719151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.612196821
Min length6

Characters and Unicode

Total characters2404084
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique306 ?
Unique (%)0.1%

Sample

1st rowCRI.2_1
2nd rowUSA.2_1
3rd rowUSA.47_1
4th rowDMA.4_1
5th rowCAN.11_1
ValueCountFrequency (%)
usa.5_1 21189
 
6.7%
usa.6_1 19719
 
6.2%
usa.47_1 14927
 
4.7%
usa.3_1 11623
 
3.7%
usa.44_1 9899
 
3.1%
usa.21_1 8906
 
2.8%
usa.10_1 8599
 
2.7%
usa.15_1 7690
 
2.4%
usa.48_1 6994
 
2.2%
can.13_1 6708
 
2.1%
Other values (1985) 199566
63.2%
2025-01-07T10:42:04.985281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 418331
17.4%
_ 315801
13.1%
. 315702
13.1%
A 237898
9.9%
U 211047
8.8%
S 206822
8.6%
4 78148
 
3.3%
3 75297
 
3.1%
2 65418
 
2.7%
5 50656
 
2.1%
Other values (28) 428964
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2404084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 418331
17.4%
_ 315801
13.1%
. 315702
13.1%
A 237898
9.9%
U 211047
8.8%
S 206822
8.6%
4 78148
 
3.3%
3 75297
 
3.1%
2 65418
 
2.7%
5 50656
 
2.1%
Other values (28) 428964
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2404084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 418331
17.4%
_ 315801
13.1%
. 315702
13.1%
A 237898
9.9%
U 211047
8.8%
S 206822
8.6%
4 78148
 
3.3%
3 75297
 
3.1%
2 65418
 
2.7%
5 50656
 
2.1%
Other values (28) 428964
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2404084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 418331
17.4%
_ 315801
13.1%
. 315702
13.1%
A 237898
9.9%
U 211047
8.8%
S 206822
8.6%
4 78148
 
3.3%
3 75297
 
3.1%
2 65418
 
2.7%
5 50656
 
2.1%
Other values (28) 428964
17.8%

level1Name
Text

Missing 

Distinct1914
Distinct (%)0.6%
Missing288804
Missing (%)47.8%
Memory size4.6 MiB
2025-01-07T10:42:05.166752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length30
Mean length8.767492448
Min length3

Characters and Unicode

Total characters2768967
Distinct characters117
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique289 ?
Unique (%)0.1%

Sample

1st rowCartago
2nd rowAlaska
3rd rowVirginia
4th rowSaint John
5th rowQuébec
ValueCountFrequency (%)
california 21273
 
5.5%
virginia 20864
 
5.4%
colorado 19719
 
5.1%
new 13960
 
3.6%
arizona 11623
 
3.0%
texas 9899
 
2.5%
maryland 8907
 
2.3%
florida 8599
 
2.2%
indiana 7690
 
2.0%
washington 6994
 
1.8%
Other values (2081) 260344
66.8%
2025-01-07T10:42:05.406921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 391229
14.1%
i 264505
 
9.6%
o 236806
 
8.6%
n 222321
 
8.0%
r 191355
 
6.9%
e 138677
 
5.0%
s 124817
 
4.5%
l 116315
 
4.2%
t 92138
 
3.3%
d 75412
 
2.7%
Other values (107) 915392
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2768967
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 391229
14.1%
i 264505
 
9.6%
o 236806
 
8.6%
n 222321
 
8.0%
r 191355
 
6.9%
e 138677
 
5.0%
s 124817
 
4.5%
l 116315
 
4.2%
t 92138
 
3.3%
d 75412
 
2.7%
Other values (107) 915392
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2768967
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 391229
14.1%
i 264505
 
9.6%
o 236806
 
8.6%
n 222321
 
8.0%
r 191355
 
6.9%
e 138677
 
5.0%
s 124817
 
4.5%
l 116315
 
4.2%
t 92138
 
3.3%
d 75412
 
2.7%
Other values (107) 915392
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2768967
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 391229
14.1%
i 264505
 
9.6%
o 236806
 
8.6%
n 222321
 
8.0%
r 191355
 
6.9%
e 138677
 
5.0%
s 124817
 
4.5%
l 116315
 
4.2%
t 92138
 
3.3%
d 75412
 
2.7%
Other values (107) 915392
33.1%

level2Gid
Text

Missing 

Distinct8078
Distinct (%)2.6%
Missing297499
Missing (%)49.2%
Memory size4.6 MiB
2025-01-07T10:42:05.620725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.27947722
Min length7

Characters and Unicode

Total characters3157105
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1940 ?
Unique (%)0.6%

Sample

1st rowCRI.2.8_1
2nd rowUSA.2.2_1
3rd rowUSA.47.124_1
4th rowCAN.11.63_1
5th rowDEU.1.20_1
ValueCountFrequency (%)
usa.6.7_1 6808
 
2.2%
usa.6.11_1 6752
 
2.2%
can.13.1_1 6708
 
2.2%
usa.3.2_1 4440
 
1.4%
usa.5.55_1 3202
 
1.0%
usa.47.40_1 2960
 
1.0%
usa.50.54_1 2928
 
1.0%
usa.21.15_1 2888
 
0.9%
usa.21.16_1 2564
 
0.8%
usa.3.11_1 2272
 
0.7%
Other values (8068) 265605
86.5%
2025-01-07T10:42:05.972864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 614117
19.5%
1 522032
16.5%
_ 307127
9.7%
A 235664
 
7.5%
U 210206
 
6.7%
S 205913
 
6.5%
2 149564
 
4.7%
3 133157
 
4.2%
4 124441
 
3.9%
5 100316
 
3.2%
Other values (28) 554568
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3157105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 614117
19.5%
1 522032
16.5%
_ 307127
9.7%
A 235664
 
7.5%
U 210206
 
6.7%
S 205913
 
6.5%
2 149564
 
4.7%
3 133157
 
4.2%
4 124441
 
3.9%
5 100316
 
3.2%
Other values (28) 554568
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3157105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 614117
19.5%
1 522032
16.5%
_ 307127
9.7%
A 235664
 
7.5%
U 210206
 
6.7%
S 205913
 
6.5%
2 149564
 
4.7%
3 133157
 
4.2%
4 124441
 
3.9%
5 100316
 
3.2%
Other values (28) 554568
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3157105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 614117
19.5%
1 522032
16.5%
_ 307127
9.7%
A 235664
 
7.5%
U 210206
 
6.7%
S 205913
 
6.5%
2 149564
 
4.7%
3 133157
 
4.2%
4 124441
 
3.9%
5 100316
 
3.2%
Other values (28) 554568
17.6%

level2Name
Text

Missing 

Distinct6808
Distinct (%)2.2%
Missing297510
Missing (%)49.2%
Memory size4.6 MiB
2025-01-07T10:42:06.169110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length29
Mean length8.485347556
Min length1

Characters and Unicode

Total characters2605986
Distinct characters155
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1657 ?
Unique (%)0.5%

Sample

1st rowTurrialba
2nd rowAleutians West
3rd rowVirginia Beach
4th rowLes Collines-de-l'Outaouais
5th rowKarlsruhe (Stadtkreis)
ValueCountFrequency (%)
san 7963
 
2.0%
boulder 6808
 
1.7%
clear 6752
 
1.7%
creek 6752
 
1.7%
yukon 6708
 
1.7%
montgomery 4776
 
1.2%
cochise 4440
 
1.1%
of 3305
 
0.8%
tuolumne 3202
 
0.8%
prince 3200
 
0.8%
Other values (7084) 336748
86.2%
2025-01-07T10:42:06.441981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 290297
 
11.1%
e 228930
 
8.8%
o 196468
 
7.5%
n 191186
 
7.3%
r 177117
 
6.8%
i 155521
 
6.0%
l 123512
 
4.7%
t 99678
 
3.8%
s 96666
 
3.7%
u 90720
 
3.5%
Other values (145) 955891
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2605986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 290297
 
11.1%
e 228930
 
8.8%
o 196468
 
7.5%
n 191186
 
7.3%
r 177117
 
6.8%
i 155521
 
6.0%
l 123512
 
4.7%
t 99678
 
3.8%
s 96666
 
3.7%
u 90720
 
3.5%
Other values (145) 955891
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2605986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 290297
 
11.1%
e 228930
 
8.8%
o 196468
 
7.5%
n 191186
 
7.3%
r 177117
 
6.8%
i 155521
 
6.0%
l 123512
 
4.7%
t 99678
 
3.8%
s 96666
 
3.7%
u 90720
 
3.5%
Other values (145) 955891
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2605986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 290297
 
11.1%
e 228930
 
8.8%
o 196468
 
7.5%
n 191186
 
7.3%
r 177117
 
6.8%
i 155521
 
6.0%
l 123512
 
4.7%
t 99678
 
3.8%
s 96666
 
3.7%
u 90720
 
3.5%
Other values (145) 955891
36.7%

level3Gid
Text

Missing 

Distinct4043
Distinct (%)6.3%
Missing540301
Missing (%)89.4%
Memory size4.6 MiB
2025-01-07T10:42:06.640245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length15
Mean length11.95808784
Min length11

Characters and Unicode

Total characters769204
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1332 ?
Unique (%)2.1%

Sample

1st rowCRI.2.8.2_1
2nd rowCAN.11.63.6_1
3rd rowDEU.1.20.1_1
4th rowCHE.10.8.10_1
5th rowZAF.9.4.1_1
ValueCountFrequency (%)
can.13.1.35_1 6689
 
10.4%
mmr.14.2.1_1 1323
 
2.1%
gbr.1.98.1_1 1301
 
2.0%
sen.1.3.3_1 961
 
1.5%
ind.31.3.1_1 744
 
1.2%
deu.1.20.1_1 733
 
1.1%
can.11.86.2_1 690
 
1.1%
idn.9.16.3_1 658
 
1.0%
per.18.1.3_1 654
 
1.0%
cri.2.7.3_1 505
 
0.8%
Other values (4033) 50067
77.8%
2025-01-07T10:42:06.896163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 192969
25.1%
1 144312
18.8%
_ 64323
 
8.4%
3 43315
 
5.6%
2 37003
 
4.8%
N 29353
 
3.8%
C 28099
 
3.7%
A 23531
 
3.1%
4 20955
 
2.7%
5 19011
 
2.5%
Other values (31) 166333
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 769204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 192969
25.1%
1 144312
18.8%
_ 64323
 
8.4%
3 43315
 
5.6%
2 37003
 
4.8%
N 29353
 
3.8%
C 28099
 
3.7%
A 23531
 
3.1%
4 20955
 
2.7%
5 19011
 
2.5%
Other values (31) 166333
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 769204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 192969
25.1%
1 144312
18.8%
_ 64323
 
8.4%
3 43315
 
5.6%
2 37003
 
4.8%
N 29353
 
3.8%
C 28099
 
3.7%
A 23531
 
3.1%
4 20955
 
2.7%
5 19011
 
2.5%
Other values (31) 166333
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 769204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 192969
25.1%
1 144312
18.8%
_ 64323
 
8.4%
3 43315
 
5.6%
2 37003
 
4.8%
N 29353
 
3.8%
C 28099
 
3.7%
A 23531
 
3.1%
4 20955
 
2.7%
5 19011
 
2.5%
Other values (31) 166333
21.6%

level3Name
Text

Missing 

Distinct3911
Distinct (%)6.2%
Missing541181
Missing (%)89.5%
Memory size4.6 MiB
2025-01-07T10:42:07.098841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length28
Mean length10.42589645
Min length2

Characters and Unicode

Total characters661471
Distinct characters124
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1274 ?
Unique (%)2.0%

Sample

1st rowLa Isabel
2nd rowPontiac
3rd rowKarlsruhe
4th rowMesocco
5th rowBitou
ValueCountFrequency (%)
unorganized 7206
 
7.7%
yukon 6689
 
7.2%
bokpyin 1323
 
1.4%
elmbridge 1301
 
1.4%
san 1275
 
1.4%
thiaroye 961
 
1.0%
n.a 819
 
0.9%
la 758
 
0.8%
coimbatore 744
 
0.8%
karlsruhe 733
 
0.8%
Other values (4216) 71692
76.7%
2025-01-07T10:42:07.370764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 74787
 
11.3%
n 56156
 
8.5%
o 51299
 
7.8%
e 42650
 
6.4%
r 41094
 
6.2%
i 40471
 
6.1%
30056
 
4.5%
u 25946
 
3.9%
l 20742
 
3.1%
t 20128
 
3.0%
Other values (114) 258142
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 661471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 74787
 
11.3%
n 56156
 
8.5%
o 51299
 
7.8%
e 42650
 
6.4%
r 41094
 
6.2%
i 40471
 
6.1%
30056
 
4.5%
u 25946
 
3.9%
l 20742
 
3.1%
t 20128
 
3.0%
Other values (114) 258142
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 661471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 74787
 
11.3%
n 56156
 
8.5%
o 51299
 
7.8%
e 42650
 
6.4%
r 41094
 
6.2%
i 40471
 
6.1%
30056
 
4.5%
u 25946
 
3.9%
l 20742
 
3.1%
t 20128
 
3.0%
Other values (114) 258142
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 661471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 74787
 
11.3%
n 56156
 
8.5%
o 51299
 
7.8%
e 42650
 
6.4%
r 41094
 
6.2%
i 40471
 
6.1%
30056
 
4.5%
u 25946
 
3.9%
l 20742
 
3.1%
t 20128
 
3.0%
Other values (114) 258142
39.0%

iucnRedListCategory
Text

Missing 

Distinct10
Distinct (%)< 0.1%
Missing96088
Missing (%)15.9%
Memory size4.6 MiB
2025-01-07T10:42:07.432746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length2
Mean length2.000086523
Min length2

Characters and Unicode

Total characters1017120
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowNE
2nd rowNE
3rd rowLC
4th rowNE
5th rowNE
ValueCountFrequency (%)
ne 354480
69.7%
lc 142489
28.0%
vu 5303
 
1.0%
dd 2469
 
0.5%
cr 2099
 
0.4%
en 933
 
0.2%
nt 742
 
0.1%
ex 21
 
< 0.1%
2024-12-02t13:57:01.149z 1
 
< 0.1%
2024-12-02t13:57:17.314z 1
 
< 0.1%
2025-01-07T10:42:07.532823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 356155
35.0%
E 355434
34.9%
C 144588
14.2%
L 142489
14.0%
V 5303
 
0.5%
U 5303
 
0.5%
D 4938
 
0.5%
R 2099
 
0.2%
T 744
 
0.1%
X 21
 
< 0.1%
Other values (12) 46
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1017120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 356155
35.0%
E 355434
34.9%
C 144588
14.2%
L 142489
14.0%
V 5303
 
0.5%
U 5303
 
0.5%
D 4938
 
0.5%
R 2099
 
0.2%
T 744
 
0.1%
X 21
 
< 0.1%
Other values (12) 46
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1017120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 356155
35.0%
E 355434
34.9%
C 144588
14.2%
L 142489
14.0%
V 5303
 
0.5%
U 5303
 
0.5%
D 4938
 
0.5%
R 2099
 
0.2%
T 744
 
0.1%
X 21
 
< 0.1%
Other values (12) 46
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1017120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 356155
35.0%
E 355434
34.9%
C 144588
14.2%
L 142489
14.0%
V 5303
 
0.5%
U 5303
 
0.5%
D 4938
 
0.5%
R 2099
 
0.2%
T 744
 
0.1%
X 21
 
< 0.1%
Other values (12) 46
 
< 0.1%

Unnamed: 223
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

Unnamed: 224
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

Unnamed: 225
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

Unnamed: 226
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

Unnamed: 227
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

Unnamed: 228
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:07.604013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length153
Median length144
Mean length144
Min length135

Characters and Unicode

Total characters288
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_HIGHERRANK
2nd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84;GEODETIC_DATUM_INVALID;CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_FUZZY
ValueCountFrequency (%)
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_derived_from_coordinates;taxon_match_higherrank 1
50.0%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;geodetic_datum_invalid;continent_derived_from_coordinates;taxon_match_fuzzy 1
50.0%
2025-01-07T10:42:07.731585image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 28
 
9.7%
E 25
 
8.7%
T 22
 
7.6%
D 21
 
7.3%
I 20
 
6.9%
N 20
 
6.9%
O 19
 
6.6%
R 18
 
6.2%
C 17
 
5.9%
A 17
 
5.9%
Other values (16) 81
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 28
 
9.7%
E 25
 
8.7%
T 22
 
7.6%
D 21
 
7.3%
I 20
 
6.9%
N 20
 
6.9%
O 19
 
6.6%
R 18
 
6.2%
C 17
 
5.9%
A 17
 
5.9%
Other values (16) 81
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 28
 
9.7%
E 25
 
8.7%
T 22
 
7.6%
D 21
 
7.3%
I 20
 
6.9%
N 20
 
6.9%
O 19
 
6.6%
R 18
 
6.2%
C 17
 
5.9%
A 17
 
5.9%
Other values (16) 81
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 28
 
9.7%
E 25
 
8.7%
T 22
 
7.6%
D 21
 
7.3%
I 20
 
6.9%
N 20
 
6.9%
O 19
 
6.6%
R 18
 
6.2%
C 17
 
5.9%
A 17
 
5.9%
Other values (16) 81
28.1%

Unnamed: 229
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:07.783794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters32
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowStillImage;StillImage;StillImage
ValueCountFrequency (%)
stillimage;stillimage;stillimage 1
100.0%
2025-01-07T10:42:07.884347image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 6
18.8%
S 3
9.4%
t 3
9.4%
i 3
9.4%
I 3
9.4%
m 3
9.4%
a 3
9.4%
g 3
9.4%
e 3
9.4%
; 2
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 6
18.8%
S 3
9.4%
t 3
9.4%
i 3
9.4%
I 3
9.4%
m 3
9.4%
a 3
9.4%
g 3
9.4%
e 3
9.4%
; 2
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 6
18.8%
S 3
9.4%
t 3
9.4%
i 3
9.4%
I 3
9.4%
m 3
9.4%
a 3
9.4%
g 3
9.4%
e 3
9.4%
; 2
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 6
18.8%
S 3
9.4%
t 3
9.4%
i 3
9.4%
I 3
9.4%
m 3
9.4%
a 3
9.4%
g 3
9.4%
e 3
9.4%
; 2
 
6.2%

Unnamed: 230
Boolean

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
True
 
2
(Missing)
604624 
ValueCountFrequency (%)
True 2
 
< 0.1%
(Missing) 604624
> 99.9%
2025-01-07T10:42:07.938855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 231
Boolean

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
False
 
2
(Missing)
604624 
ValueCountFrequency (%)
False 2
 
< 0.1%
(Missing) 604624
> 99.9%
2025-01-07T10:42:07.978854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 232
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4584679.5
Minimum1364691
Maximum7804668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:08.015258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1364691
5-th percentile1686689.85
Q12974685.25
median4584679.5
Q36194673.75
95-th percentile7482669.15
Maximum7804668
Range6439977
Interquartile range (IQR)3219988.5

Descriptive statistics

Standard deviation4553751.407
Coefficient of variation (CV)0.9932540339
Kurtosisnan
Mean4584679.5
Median Absolute Deviation (MAD)3219988.5
Skewnessnan
Sum9169359
Variance2.073665188 × 1013
MonotonicityStrictly decreasing
2025-01-07T10:42:08.059764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
7804668 1
 
< 0.1%
1364691 1
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
1364691 1
< 0.1%
7804668 1
< 0.1%
ValueCountFrequency (%)
7804668 1
< 0.1%
1364691 1
< 0.1%

Unnamed: 233
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4584679.5
Minimum1364691
Maximum7804668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:08.103316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1364691
5-th percentile1686689.85
Q12974685.25
median4584679.5
Q36194673.75
95-th percentile7482669.15
Maximum7804668
Range6439977
Interquartile range (IQR)3219988.5

Descriptive statistics

Standard deviation4553751.407
Coefficient of variation (CV)0.9932540339
Kurtosisnan
Mean4584679.5
Median Absolute Deviation (MAD)3219988.5
Skewnessnan
Sum9169359
Variance2.073665188 × 1013
MonotonicityStrictly decreasing
2025-01-07T10:42:08.148825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
7804668 1
 
< 0.1%
1364691 1
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
1364691 1
< 0.1%
7804668 1
< 0.1%
ValueCountFrequency (%)
7804668 1
< 0.1%
1364691 1
< 0.1%

Unnamed: 234
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:08.193157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosisnan
Mean1
Median Absolute Deviation (MAD)0
Skewnessnan
Sum2
Variance0
MonotonicityIncreasing
2025-01-07T10:42:08.235666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 2
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
1 2
< 0.1%
ValueCountFrequency (%)
1 2
< 0.1%

Unnamed: 235
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean54
Minimum54
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:08.278214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile54
Q154
median54
Q354
95-th percentile54
Maximum54
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosisnan
Mean54
Median Absolute Deviation (MAD)0
Skewnessnan
Sum108
Variance0
MonotonicityIncreasing
2025-01-07T10:42:08.320264image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
54 2
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
54 2
< 0.1%
ValueCountFrequency (%)
54 2
< 0.1%

Unnamed: 236
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean216
Minimum216
Maximum216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:08.361859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum216
5-th percentile216
Q1216
median216
Q3216
95-th percentile216
Maximum216
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosisnan
Mean216
Median Absolute Deviation (MAD)0
Skewnessnan
Sum432
Variance0
MonotonicityIncreasing
2025-01-07T10:42:08.402964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
216 2
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
216 2
< 0.1%
ValueCountFrequency (%)
216 2
< 0.1%

Unnamed: 237
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1463.5
Minimum1457
Maximum1470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:08.443470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1457
5-th percentile1457.65
Q11460.25
median1463.5
Q31466.75
95-th percentile1469.35
Maximum1470
Range13
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation9.192388155
Coefficient of variation (CV)0.006281098842
Kurtosisnan
Mean1463.5
Median Absolute Deviation (MAD)6.5
Skewnessnan
Sum2927
Variance84.5
MonotonicityStrictly decreasing
2025-01-07T10:42:08.491186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1470 1
 
< 0.1%
1457 1
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
1457 1
< 0.1%
1470 1
< 0.1%
ValueCountFrequency (%)
1470 1
< 0.1%
1457 1
< 0.1%

Unnamed: 238
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7608.5
Minimum5503
Maximum9714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:08.533696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5503
5-th percentile5713.55
Q16555.75
median7608.5
Q38661.25
95-th percentile9503.45
Maximum9714
Range4211
Interquartile range (IQR)2105.5

Descriptive statistics

Standard deviation2977.626656
Coefficient of variation (CV)0.391355281
Kurtosisnan
Mean7608.5
Median Absolute Deviation (MAD)2105.5
Skewnessnan
Sum15217
Variance8866260.5
MonotonicityStrictly decreasing
2025-01-07T10:42:08.573698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
9714 1
 
< 0.1%
5503 1
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
5503 1
< 0.1%
9714 1
< 0.1%
ValueCountFrequency (%)
9714 1
< 0.1%
5503 1
< 0.1%

Unnamed: 239
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4584321
Minimum1363974
Maximum7804668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:08.613211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1363974
5-th percentile1686008.7
Q12974147.5
median4584321
Q36194494.5
95-th percentile7482633.3
Maximum7804668
Range6440694
Interquartile range (IQR)3220347

Descriptive statistics

Standard deviation4554258.403
Coefficient of variation (CV)0.993442301
Kurtosisnan
Mean4584321
Median Absolute Deviation (MAD)3220347
Skewnessnan
Sum9168642
Variance2.07412696 × 1013
MonotonicityStrictly decreasing
2025-01-07T10:42:08.665740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
7804668 1
 
< 0.1%
1363974 1
 
< 0.1%
(Missing) 604624
> 99.9%
ValueCountFrequency (%)
1363974 1
< 0.1%
7804668 1
< 0.1%
ValueCountFrequency (%)
7804668 1
< 0.1%
1363974 1
< 0.1%

Unnamed: 240
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

Unnamed: 241
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1364691
Minimum1364691
Maximum1364691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-01-07T10:42:08.714842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1364691
5-th percentile1364691
Q11364691
median1364691
Q31364691
95-th percentile1364691
Maximum1364691
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean1364691
Median Absolute Deviation (MAD)0
Skewnessnan
Sum1364691
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:42:08.760348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1364691 1
 
< 0.1%
(Missing) 604625
> 99.9%
ValueCountFrequency (%)
1364691 1
< 0.1%
ValueCountFrequency (%)
1364691 1
< 0.1%

Unnamed: 242
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:08.791849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters14
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowAphytis roseni
ValueCountFrequency (%)
aphytis 1
50.0%
roseni 1
50.0%
2025-01-07T10:42:08.886498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 2
14.3%
i 2
14.3%
A 1
7.1%
p 1
7.1%
y 1
7.1%
h 1
7.1%
t 1
7.1%
1
7.1%
r 1
7.1%
o 1
7.1%
Other values (2) 2
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 2
14.3%
i 2
14.3%
A 1
7.1%
p 1
7.1%
y 1
7.1%
h 1
7.1%
t 1
7.1%
1
7.1%
r 1
7.1%
o 1
7.1%
Other values (2) 2
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 2
14.3%
i 2
14.3%
A 1
7.1%
p 1
7.1%
y 1
7.1%
h 1
7.1%
t 1
7.1%
1
7.1%
r 1
7.1%
o 1
7.1%
Other values (2) 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 2
14.3%
i 2
14.3%
A 1
7.1%
p 1
7.1%
y 1
7.1%
h 1
7.1%
t 1
7.1%
1
7.1%
r 1
7.1%
o 1
7.1%
Other values (2) 2
14.3%

Unnamed: 243
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:08.947011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length35
Median length29
Mean length29
Min length23

Characters and Unicode

Total characters58
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowTrogoderma Dejean, 1821
2nd rowAphytis roseni DeBach & Gordh, 1974
ValueCountFrequency (%)
trogoderma 1
11.1%
dejean 1
11.1%
1821 1
11.1%
aphytis 1
11.1%
roseni 1
11.1%
debach 1
11.1%
1
11.1%
gordh 1
11.1%
1974 1
11.1%
2025-01-07T10:42:09.057708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7
 
12.1%
e 5
 
8.6%
o 4
 
6.9%
r 4
 
6.9%
a 3
 
5.2%
1 3
 
5.2%
h 3
 
5.2%
, 2
 
3.4%
d 2
 
3.4%
n 2
 
3.4%
Other values (20) 23
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7
 
12.1%
e 5
 
8.6%
o 4
 
6.9%
r 4
 
6.9%
a 3
 
5.2%
1 3
 
5.2%
h 3
 
5.2%
, 2
 
3.4%
d 2
 
3.4%
n 2
 
3.4%
Other values (20) 23
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7
 
12.1%
e 5
 
8.6%
o 4
 
6.9%
r 4
 
6.9%
a 3
 
5.2%
1 3
 
5.2%
h 3
 
5.2%
, 2
 
3.4%
d 2
 
3.4%
n 2
 
3.4%
Other values (20) 23
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7
 
12.1%
e 5
 
8.6%
o 4
 
6.9%
r 4
 
6.9%
a 3
 
5.2%
1 3
 
5.2%
h 3
 
5.2%
, 2
 
3.4%
d 2
 
3.4%
n 2
 
3.4%
Other values (20) 23
39.7%

Unnamed: 244
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:09.109999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length18
Mean length18
Min length14

Characters and Unicode

Total characters36
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowTrogoderma obsolescens
2nd rowAphtyis roseni
ValueCountFrequency (%)
trogoderma 1
25.0%
obsolescens 1
25.0%
aphtyis 1
25.0%
roseni 1
25.0%
2025-01-07T10:42:09.276379image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 5
13.9%
s 5
13.9%
e 4
 
11.1%
r 3
 
8.3%
i 2
 
5.6%
n 2
 
5.6%
2
 
5.6%
T 1
 
2.8%
g 1
 
2.8%
m 1
 
2.8%
Other values (10) 10
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5
13.9%
s 5
13.9%
e 4
 
11.1%
r 3
 
8.3%
i 2
 
5.6%
n 2
 
5.6%
2
 
5.6%
T 1
 
2.8%
g 1
 
2.8%
m 1
 
2.8%
Other values (10) 10
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5
13.9%
s 5
13.9%
e 4
 
11.1%
r 3
 
8.3%
i 2
 
5.6%
n 2
 
5.6%
2
 
5.6%
T 1
 
2.8%
g 1
 
2.8%
m 1
 
2.8%
Other values (10) 10
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5
13.9%
s 5
13.9%
e 4
 
11.1%
r 3
 
8.3%
i 2
 
5.6%
n 2
 
5.6%
2
 
5.6%
T 1
 
2.8%
g 1
 
2.8%
m 1
 
2.8%
Other values (10) 10
27.8%

Unnamed: 245
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

Unnamed: 246
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:09.365076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEML
2nd rowEML
ValueCountFrequency (%)
eml 2
100.0%
2025-01-07T10:42:09.477342image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2
33.3%
M 2
33.3%
L 2
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2
33.3%
M 2
33.3%
L 2
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2
33.3%
M 2
33.3%
L 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2
33.3%
M 2
33.3%
L 2
33.3%

Unnamed: 247
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:09.528096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters48
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row2024-12-02T13:57:01.149Z
2nd row2024-12-02T13:57:17.314Z
ValueCountFrequency (%)
2024-12-02t13:57:01.149z 1
50.0%
2024-12-02t13:57:17.314z 1
50.0%
2025-01-07T10:42:09.628552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 8
16.7%
1 8
16.7%
0 5
10.4%
4 4
8.3%
- 4
8.3%
: 4
8.3%
3 3
 
6.2%
7 3
 
6.2%
T 2
 
4.2%
5 2
 
4.2%
Other values (3) 5
10.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 8
16.7%
1 8
16.7%
0 5
10.4%
4 4
8.3%
- 4
8.3%
: 4
8.3%
3 3
 
6.2%
7 3
 
6.2%
T 2
 
4.2%
5 2
 
4.2%
Other values (3) 5
10.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 8
16.7%
1 8
16.7%
0 5
10.4%
4 4
8.3%
- 4
8.3%
: 4
8.3%
3 3
 
6.2%
7 3
 
6.2%
T 2
 
4.2%
5 2
 
4.2%
Other values (3) 5
10.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 8
16.7%
1 8
16.7%
0 5
10.4%
4 4
8.3%
- 4
8.3%
: 4
8.3%
3 3
 
6.2%
7 3
 
6.2%
T 2
 
4.2%
5 2
 
4.2%
Other values (3) 5
10.4%

Unnamed: 248
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:09.783489image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters48
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-12-02T11:48:23.416Z
2nd row2024-12-02T11:48:23.416Z
ValueCountFrequency (%)
2024-12-02t11:48:23.416z 2
100.0%
2025-01-07T10:42:09.887972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10
20.8%
1 8
16.7%
4 6
12.5%
0 4
 
8.3%
- 4
 
8.3%
: 4
 
8.3%
T 2
 
4.2%
8 2
 
4.2%
3 2
 
4.2%
. 2
 
4.2%
Other values (2) 4
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 10
20.8%
1 8
16.7%
4 6
12.5%
0 4
 
8.3%
- 4
 
8.3%
: 4
 
8.3%
T 2
 
4.2%
8 2
 
4.2%
3 2
 
4.2%
. 2
 
4.2%
Other values (2) 4
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 10
20.8%
1 8
16.7%
4 6
12.5%
0 4
 
8.3%
- 4
 
8.3%
: 4
 
8.3%
T 2
 
4.2%
8 2
 
4.2%
3 2
 
4.2%
. 2
 
4.2%
Other values (2) 4
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 10
20.8%
1 8
16.7%
4 6
12.5%
0 4
 
8.3%
- 4
 
8.3%
: 4
 
8.3%
T 2
 
4.2%
8 2
 
4.2%
3 2
 
4.2%
. 2
 
4.2%
Other values (2) 4
 
8.3%

Unnamed: 249
Boolean

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
False
 
1
True
 
1
(Missing)
604624 
ValueCountFrequency (%)
False 1
 
< 0.1%
True 1
 
< 0.1%
(Missing) 604624
> 99.9%
2025-01-07T10:42:09.942822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 250
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

Unnamed: 251
Unsupported

Missing  Rejected  Unsupported 

Missing604626
Missing (%)100.0%
Memory size4.6 MiB

Unnamed: 252
Boolean

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
False
 
2
(Missing)
604624 
ValueCountFrequency (%)
False 2
 
< 0.1%
(Missing) 604624
> 99.9%
2025-01-07T10:42:09.985812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 253
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:10.017814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters26
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowNORTH_AMERICA
2nd rowLATIN_AMERICA
ValueCountFrequency (%)
north_america 1
50.0%
latin_america 1
50.0%
2025-01-07T10:42:10.111830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 5
19.2%
R 3
11.5%
I 3
11.5%
N 2
 
7.7%
C 2
 
7.7%
T 2
 
7.7%
M 2
 
7.7%
_ 2
 
7.7%
E 2
 
7.7%
O 1
 
3.8%
Other values (2) 2
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5
19.2%
R 3
11.5%
I 3
11.5%
N 2
 
7.7%
C 2
 
7.7%
T 2
 
7.7%
M 2
 
7.7%
_ 2
 
7.7%
E 2
 
7.7%
O 1
 
3.8%
Other values (2) 2
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5
19.2%
R 3
11.5%
I 3
11.5%
N 2
 
7.7%
C 2
 
7.7%
T 2
 
7.7%
M 2
 
7.7%
_ 2
 
7.7%
E 2
 
7.7%
O 1
 
3.8%
Other values (2) 2
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5
19.2%
R 3
11.5%
I 3
11.5%
N 2
 
7.7%
C 2
 
7.7%
T 2
 
7.7%
M 2
 
7.7%
_ 2
 
7.7%
E 2
 
7.7%
O 1
 
3.8%
Other values (2) 2
 
7.7%

Unnamed: 254
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:10.157334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters26
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 2
100.0%
2025-01-07T10:42:10.249365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 4
15.4%
A 4
15.4%
N 2
7.7%
O 2
7.7%
T 2
7.7%
H 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
I 2
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 4
15.4%
A 4
15.4%
N 2
7.7%
O 2
7.7%
T 2
7.7%
H 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
I 2
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 4
15.4%
A 4
15.4%
N 2
7.7%
O 2
7.7%
T 2
7.7%
H 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
I 2
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 4
15.4%
A 4
15.4%
N 2
7.7%
O 2
7.7%
T 2
7.7%
H 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
I 2
7.7%

Unnamed: 255
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:10.289639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowUSA
2nd rowPER
ValueCountFrequency (%)
usa 1
50.0%
per 1
50.0%
2025-01-07T10:42:10.381695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 1
16.7%
S 1
16.7%
A 1
16.7%
P 1
16.7%
E 1
16.7%
R 1
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1
16.7%
S 1
16.7%
A 1
16.7%
P 1
16.7%
E 1
16.7%
R 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1
16.7%
S 1
16.7%
A 1
16.7%
P 1
16.7%
E 1
16.7%
R 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1
16.7%
S 1
16.7%
A 1
16.7%
P 1
16.7%
E 1
16.7%
R 1
16.7%

Unnamed: 256
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:10.422437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length8.5
Mean length8.5
Min length4

Characters and Unicode

Total characters17
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowUnited States
2nd rowPeru
ValueCountFrequency (%)
united 1
33.3%
states 1
33.3%
peru 1
33.3%
2025-01-07T10:42:10.515428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3
17.6%
t 3
17.6%
U 1
 
5.9%
i 1
 
5.9%
n 1
 
5.9%
d 1
 
5.9%
1
 
5.9%
S 1
 
5.9%
a 1
 
5.9%
s 1
 
5.9%
Other values (3) 3
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3
17.6%
t 3
17.6%
U 1
 
5.9%
i 1
 
5.9%
n 1
 
5.9%
d 1
 
5.9%
1
 
5.9%
S 1
 
5.9%
a 1
 
5.9%
s 1
 
5.9%
Other values (3) 3
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3
17.6%
t 3
17.6%
U 1
 
5.9%
i 1
 
5.9%
n 1
 
5.9%
d 1
 
5.9%
1
 
5.9%
S 1
 
5.9%
a 1
 
5.9%
s 1
 
5.9%
Other values (3) 3
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3
17.6%
t 3
17.6%
U 1
 
5.9%
i 1
 
5.9%
n 1
 
5.9%
d 1
 
5.9%
1
 
5.9%
S 1
 
5.9%
a 1
 
5.9%
s 1
 
5.9%
Other values (3) 3
17.6%

Unnamed: 257
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:10.556932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowUSA.10_1
2nd rowPER.17_1
ValueCountFrequency (%)
usa.10_1 1
50.0%
per.17_1 1
50.0%
2025-01-07T10:42:10.647983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4
25.0%
. 2
12.5%
_ 2
12.5%
A 1
 
6.2%
S 1
 
6.2%
U 1
 
6.2%
0 1
 
6.2%
P 1
 
6.2%
E 1
 
6.2%
R 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4
25.0%
. 2
12.5%
_ 2
12.5%
A 1
 
6.2%
S 1
 
6.2%
U 1
 
6.2%
0 1
 
6.2%
P 1
 
6.2%
E 1
 
6.2%
R 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4
25.0%
. 2
12.5%
_ 2
12.5%
A 1
 
6.2%
S 1
 
6.2%
U 1
 
6.2%
0 1
 
6.2%
P 1
 
6.2%
E 1
 
6.2%
R 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4
25.0%
. 2
12.5%
_ 2
12.5%
A 1
 
6.2%
S 1
 
6.2%
U 1
 
6.2%
0 1
 
6.2%
P 1
 
6.2%
E 1
 
6.2%
R 1
 
6.2%

Unnamed: 258
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:10.692663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length6.5
Mean length6.5
Min length6

Characters and Unicode

Total characters13
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowFlorida
2nd rowLoreto
ValueCountFrequency (%)
florida 1
50.0%
loreto 1
50.0%
2025-01-07T10:42:10.786991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3
23.1%
r 2
15.4%
l 1
 
7.7%
F 1
 
7.7%
i 1
 
7.7%
d 1
 
7.7%
a 1
 
7.7%
L 1
 
7.7%
e 1
 
7.7%
t 1
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3
23.1%
r 2
15.4%
l 1
 
7.7%
F 1
 
7.7%
i 1
 
7.7%
d 1
 
7.7%
a 1
 
7.7%
L 1
 
7.7%
e 1
 
7.7%
t 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3
23.1%
r 2
15.4%
l 1
 
7.7%
F 1
 
7.7%
i 1
 
7.7%
d 1
 
7.7%
a 1
 
7.7%
L 1
 
7.7%
e 1
 
7.7%
t 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3
23.1%
r 2
15.4%
l 1
 
7.7%
F 1
 
7.7%
i 1
 
7.7%
d 1
 
7.7%
a 1
 
7.7%
L 1
 
7.7%
e 1
 
7.7%
t 1
 
7.7%

Unnamed: 259
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:10.832492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length10.5
Mean length10.5
Min length10

Characters and Unicode

Total characters21
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowUSA.10.52_1
2nd rowPER.17.1_1
ValueCountFrequency (%)
usa.10.52_1 1
50.0%
per.17.1_1 1
50.0%
2025-01-07T10:42:10.931520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5
23.8%
. 4
19.0%
_ 2
 
9.5%
A 1
 
4.8%
U 1
 
4.8%
S 1
 
4.8%
0 1
 
4.8%
5 1
 
4.8%
2 1
 
4.8%
P 1
 
4.8%
Other values (3) 3
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5
23.8%
. 4
19.0%
_ 2
 
9.5%
A 1
 
4.8%
U 1
 
4.8%
S 1
 
4.8%
0 1
 
4.8%
5 1
 
4.8%
2 1
 
4.8%
P 1
 
4.8%
Other values (3) 3
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5
23.8%
. 4
19.0%
_ 2
 
9.5%
A 1
 
4.8%
U 1
 
4.8%
S 1
 
4.8%
0 1
 
4.8%
5 1
 
4.8%
2 1
 
4.8%
P 1
 
4.8%
Other values (3) 3
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5
23.8%
. 4
19.0%
_ 2
 
9.5%
A 1
 
4.8%
U 1
 
4.8%
S 1
 
4.8%
0 1
 
4.8%
5 1
 
4.8%
2 1
 
4.8%
P 1
 
4.8%
Other values (3) 3
14.3%

Unnamed: 260
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing604624
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:10.978521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length10.5
Mean length10.5
Min length8

Characters and Unicode

Total characters21
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowPinellas
2nd rowAlto Amazonas
ValueCountFrequency (%)
pinellas 1
33.3%
alto 1
33.3%
amazonas 1
33.3%
2025-01-07T10:42:11.079804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 3
14.3%
a 3
14.3%
n 2
9.5%
s 2
9.5%
A 2
9.5%
o 2
9.5%
e 1
 
4.8%
P 1
 
4.8%
i 1
 
4.8%
t 1
 
4.8%
Other values (3) 3
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 3
14.3%
a 3
14.3%
n 2
9.5%
s 2
9.5%
A 2
9.5%
o 2
9.5%
e 1
 
4.8%
P 1
 
4.8%
i 1
 
4.8%
t 1
 
4.8%
Other values (3) 3
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 3
14.3%
a 3
14.3%
n 2
9.5%
s 2
9.5%
A 2
9.5%
o 2
9.5%
e 1
 
4.8%
P 1
 
4.8%
i 1
 
4.8%
t 1
 
4.8%
Other values (3) 3
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 3
14.3%
a 3
14.3%
n 2
9.5%
s 2
9.5%
A 2
9.5%
o 2
9.5%
e 1
 
4.8%
P 1
 
4.8%
i 1
 
4.8%
t 1
 
4.8%
Other values (3) 3
14.3%

Unnamed: 261
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:11.125260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPER.17.1.5_1
ValueCountFrequency (%)
per.17.1.5_1 1
100.0%
2025-01-07T10:42:11.217871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3
25.0%
1 3
25.0%
E 1
 
8.3%
P 1
 
8.3%
R 1
 
8.3%
7 1
 
8.3%
5 1
 
8.3%
_ 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3
25.0%
1 3
25.0%
E 1
 
8.3%
P 1
 
8.3%
R 1
 
8.3%
7 1
 
8.3%
5 1
 
8.3%
_ 1
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3
25.0%
1 3
25.0%
E 1
 
8.3%
P 1
 
8.3%
R 1
 
8.3%
7 1
 
8.3%
5 1
 
8.3%
_ 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3
25.0%
1 3
25.0%
E 1
 
8.3%
P 1
 
8.3%
R 1
 
8.3%
7 1
 
8.3%
5 1
 
8.3%
_ 1
 
8.3%

Unnamed: 262
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:11.256375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowLagunas
ValueCountFrequency (%)
lagunas 1
100.0%
2025-01-07T10:42:11.348644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
28.6%
L 1
14.3%
g 1
14.3%
u 1
14.3%
n 1
14.3%
s 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
28.6%
L 1
14.3%
g 1
14.3%
u 1
14.3%
n 1
14.3%
s 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
28.6%
L 1
14.3%
g 1
14.3%
u 1
14.3%
n 1
14.3%
s 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
28.6%
L 1
14.3%
g 1
14.3%
u 1
14.3%
n 1
14.3%
s 1
14.3%

Unnamed: 263
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing604625
Missing (%)> 99.9%
Memory size4.6 MiB
2025-01-07T10:42:11.389021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowNE
ValueCountFrequency (%)
ne 1
100.0%
2025-01-07T10:42:11.476574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1
50.0%
E 1
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1
50.0%
E 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1
50.0%
E 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1
50.0%
E 1
50.0%